1 Multi-User Cooperative Mobile Video Streaming: Performance Analysis and Online Mechanism Design

Transcription

1 Multi-User Cooperative Mobile Video Streamig: Performace Aalysis ad Olie Mechaism Desig Li Gao, Seior Member, IEEE, Mig Tag, Haitia Pag, Studet Member, IEEE, Jiawei Huag, Fellow, IEEE, ad Lifeg Su, Member, IEEE Abstract Adaptive bitrate streamig eables video users to adapt their playig bitrates to the real-time etwork coditios, hece achievig the desirable quality-of-experiece (QoE). I a multi-user wireless sceario, however, existig sigle-user based bitrate adaptatio methods may fail to provide the desirable QoE, due to lack of cosideratio of multi-user iteractios (such as the multi-user iterfereces ad etwork cogestio). I this work, we propose a ovel user cooperatio framework based o user-provided etworkig for multi-user mobile video streamig over wireless cellular etworks. The framework eables earby mobile video users to crowdsource their cellular liks ad resources for cooperative video streamig. We first aalyze the social welfare performace boud of the proposed cooperative streamig system by itroducig a virtual time-slotted system. The, we desig a low complexity Lyapuov-based olie algorithm, which ca be implemeted i a olie ad distributed maer without the complete future ad global etwork iformatio. Numerical results show that the proposed olie algorithm achieves a average 97% of the theoretical maximum social welfare. We further coduct experimets with real data traces, to compare our proposed olie algorithm with the existig olie algorithms i the literature. Experimet results show that our algorithm outperforms the existig algorithms i terms of both the achievable bitrate (with a average gai of % 3%) ad social welfare (with a average gai of % 5%). Keywords Mobile Video Streamig, Adaptive Bitrate, Mobile Crowdsourcig, Olie Algorithm F INTRODUCTION. Backgroud ad Motivatios Global mobile data traffic is growig at a uprecedeted rate, where mobile video streamig cotributes most of the data growth. Accordig to Cisco [], mobile video streamig traffic has accouted for 6% of the global mobile data traffic i 6, ad the percetage is expected to icrease to 78% by. Adaptive BitRate (ABR) streamig [] is a promisig techology for video streamig over large distributed HTTP etworks (e.g., Iteret) ad has bee adopted by may popular olie video streamig systems (e.g., HTTP dyamic streamig of Adobe [3], HTTP live streamig of Apple [4], ad smooth streamig of Microsoft [5]). The key idea of ABR is to eable video players to adapt the playig bitrate (correspodig to the quality of video, e.g., resolutio) to the real-time etwork coditios to esure the desirable quality-of-experiece (QoE). While most of the existig works focused o the bitrate adaptatio methods of a sigle user (e.g., [6], [7]), i this work we cosider a more geeral sceario of multi-user video streamig over wireless cellular etworks. I the L. Gao is with the School of Electroic ad Iformatio Egieerig, Harbi Istitute of Techology, Shezhe, Chia, gaol@hit.edu.c; M. Tag ad J. Huag are with the Network Commuicatios ad Ecoomics Lab (NCEL), Departmet of Iformatio Egieerig, The Chiese Uiversity of Hog Kog, {mtag, jwhuag}@ie.cuhk.edu.hk; H. Pag ad L. Su are with the Departmet of Computer Sciece ad Techology, Tsighua Uiversity, Chia, pht4@mails.tsighua.edu.c, sulf@tsighua.edu.c. multi-user wireless sceario, the QoE of each mobile user is affected ot oly by the stochastic chagig of his ow etwork coditio (e.g., chael fadig), but also by the potetial resource competitio ad iterferece of other users [8] [9]. Without proper coordiatio or cooperatio amog users, such competitio ad iterferece may degrade the etwork performace greatly (e.g., leadig to cogestio), hece icrease the video streamig cost ad harm the user QoE. Thus, the existig sigle-user based bitrate adaptatio methods ofte fail to provide desirable QoE for video users i the multi-user sceario, due to lack of cosideratio of multi-user competitio ad iterferece. To this ed, i this work we will study the multiuser cooperative video streamig, where (earby) mobile video users cooperate with each other i both bitrate adaptig ad video dowloadig. Namely, each user ca dowload video data for other users usig his ow cellular lik or dowload his video data through others liks. I this sese, users aggregate their cellular liks ad resources for the cooperative video streamig. Figure illustrates such a cooperative streamig model with three users {,, 3}, where user dowloads for all three users ad user dowloads for himself ad user 3. Note that user 3 does ot have the available cellular lik. There are several real-world scearios where the multiuser cooperative streamig is useful ad helpful. First, the most relevat sceario is User-Provided Networkig (UPN) [], [], where mobile devices (e.g., 4G smartphoes) with abudat cellular lik capacities operate as mobile hotspots ad provide Iteret access to other

2 3G/4G 3G/4G 3G/4G Iteret Iteret WiFi/Bluetooth Cooperative User Goup Iteret Figure. Cooperative Video Streamig Model. X 3 User 's Segmet User 's Segmet User 3's Segmet devices. UPN has bee widely studied ad implemeted today, ad some IT ad Telecom compaies (such as OpeGarde [], Karma [3], ad AT&T [4]) have provided commercial UPN services. The cooperative streamig proposed i this work ca ehace the capability of UPN o providig the video streamig service. Aother more cocrete sceario is Mobile Live Streamig (MLS) [5], [6], with which people ca watch live activities of their frieds or share their ow activities to their frieds o their smartphoes. MLS becomes popular i recet years with the proliferatio of smartphoes ad 4G cellular etworks. Nowadays, may social etwork compaies have provided MLS services, such as IgKee [7], Youtube Live [8], ad Facebook Livestream [9]. The cooperative streamig proposed i this work ca improve the live streamig quality i MLS. The key motivatio for cosiderig such a cooperative streamig system is the heterogeeity of mobile devices. Note that cooperative streamig ca be easily implemeted i a practical sceario (such as UPN ad MLS) by istallig some customized apps (e.g., OpeGarde []) o smartphoes, ad the related optimizatio ad icetive issues have bee studied i the recet literature (e.g., [], []). However, the existig techiques i [], [] caot be directly applied to the cooperative video streamig model, due to the asychroous operatios of video streamig ad the uique QoE requiremets of video applicatios. This motivates us to study the multiuser cooperative streamig i this work.. Solutio ad Cotributios I this work, we propose a geeral multi-user cooperative video streamig framework based o UPN [], []. The key idea is to eable earby mobile users to form a cooperative group (via WiFi or Bluetooth) ad aggregate their cellular liks ad resources for the cooperative video dowloadig ad bitrate adaptig. We focus o studyig the users streamig behaviours (i.e., dowload schedulig ad bitrate adaptatio) i the proposed cooperative framework. Namely, for each video user, whe. Accordig to [], smartphoes oly accout for 38% of the total mobile devices, ad a large amout of o-smartphoe mobile devices (e.g., tablets ad laptops) still lack stable ad always-o Iteret coectios, especially i the outdoor eviromet. Our proposed system ca help these devices coect to the Iteret via the liks of earby smartphoes. Furthermore, eve for the smartphoe devices with the same or similar Iteret capability, they may have differet cost evaluatios for eergy cosumptio (depedig o, for example, their battery status), resultig i certai heterogeeity amog devices. ad from whom he is goig to dowload each video segmet, at which bitrate? Our goal is to uderstad the performace boud of the system ad desig a olie schedulig method to approach such a boud. First, we formally defie the users operatios i the cooperative streamig system, ad formulate the correspodig social welfare optimizatio problem (Sectio 4). The optimal solutio of this problem provides the theoretical performace boud of the proposed cooperative streamig system. A comprehesive aalysis for such a performace boud is the foudatio of the future study o privacy, security, ad icetive mechaism desig. 3 Secod, we aalyze the social welfare performace boud of the proposed cooperative streamig system (Sectio 5). Directly solvig such a performace is challegig, due to the asychroous operatios of users as well as the mixed-iteger ature of the problem. To this ed, we itroduce a virtual time-slotted system with the sychroized operatios, ad formulate the ew social welfare optimizatio problem as a liear programmig (which ca be solved efficietly with may stadard methods). We show that with proper choices of time parameters, the optimal solutio of the virtual time-slotted system ca provide a effective upper-boud ad lower-boud for the optimal solutio (performace boud) of the origial system, which forms the feasible performace regio of the proposed cooperative streamig system. Fially, we desig a Lyapuov-based olie streamig algorithm for the practical implemetatio of the proposed cooperative streamig system (Sectio 6). The proposed algorithm coverges to the theoretical performace boud asymptotically, with a cotrollable approximatio error boud. Moreover, it relies oly o the curret state ad historical streamig iformatio (while ot o ay future etwork iformatio), hece ca be implemeted i the olie maer; ad it requires oly the local iformatio exchage withi each cooperative group (while ot the global etwork iformatio exchage), hece ca be implemeted i the distributed maer. We perform extesive experimetal simulatios with real data traces to evaluate its performace gap with the theoretical boud ad to compare its performace with state-of-art olie algorithms i the existig literature. For more clarity, we summarize the logical relatioship amog the above three parts as follows: (i) the social welfare optimizatio problem i Sectio 4 defies the theoretical performace boud of the proposed cooperative streamig sys-. The icetive issue is ot the focus of this work. Namely, we assume that some well-desiged icetive mechaisms (e.g., Nash bargaiig [3], auctio [3] [34], or others trust mechaisms [35] [38]) have bee adopted, such that video users are willig to cooperate. 3. I practice, there have bee various existig approaches to address the privacy/security issue i the similar systems. For example, Opegarde [] solves the privacy/security issue by usig advaced data ecryptio techique through built-i softwares, while Karma [3] solves it by usig hardware-eabled data ecryptio techique through dedicated devices. These existig techical ways ca help us quickly build the privacy ad security system.

3 3 tem (but it is challegig to solve); (ii) the virtual time-slotted system i Sectio 5 helps characterize the regio (i.e., upperboud ad lower-boud) of the above theoretical performace boud; (iii) the olie algorithm i Sectio 6 coverges to the above theoretical performace boud asymptotically i the realistic sceario without complete future etwork iformatio. More specifically, the key cotributios of this work are summarized as follows. Novel Model: To our best kowledge, this is the first work that proposes a geeral multi-user cooperative streamig framework for mobile video streamig. The framework eables mobile video users to crowdsource their radio coectios ad resources for cooperative video streamig, ad ca effectively improve the QoE of video users. Moreover, we provide both theoretical performace aalysis ad practical algorithm desig for such a cooperative streamig system. Performace Boud Aalysis: We aalyze the theoretical performace boud of the proposed cooperative streamig system, overcomig the challegig issue of asychroous operatios by usig a virtual time-slotted system. Such a performace boud aalysis is fudametal for the desig, evaluatio, ad implemetatio of practical algorithms i such a cooperative streamig system. Olie Algorithm Desig: We implemet the cooperative streamig system i the practical sceario without future ad global etwork iformatio, ad desig a Lyapuov-based olie streamig algorithm. The proposed algorithm coverges to the theoretical performace boud asymptotically. Experimet ad Demo: We coduct extesive experimets with real data traces, which show that our proposed cooperative streamig system, together with the olie streamig algorithm, outperforms the existig systems ad algorithms i terms of both achieved bitrate (with a average gai of % 3%) ad social welfare (with a average gai of % 5%). We also costruct a real demo system to implemet ad evaluate the proposed system ad algorithm. The rest of the paper is orgaized as follows. I Sectio, we review the related work. I Sectio 3, we preset the system model. I Sectio 4, we provide the problem formulatio. I Sectio 5, we propose the virtual timeslotted system ad the performace boud aalysis. I Sectio 6, we propose the Lyapuov-based olie streamig algorithm. We provide simulatio results i Sectio 7 ad coclude i Sectio 8. LITERATURE REVIEW Prior works o ABR video streamig maily focused o the bitrate adaptatio of a sigle user usig either bufferbased method [6] or chael predictio-based method [7]. Recetly, there is a growig iterest i exploitig the multi-user cooperative video streamig [8] [9]. From the modelig perspective, the existig cooperative streamig models ca be classified ito four categories: ) Badwidth Aggregatio (BA) model [8], [9], ) Device-to-Device (DD) model [] [3], 3) Crowdsourced Mobile Streamig (CMS) model [4] [6], ad 4) Mobile Peer-to-Peer (MPP) model [7] [9]. 4 ) BA Model [8], [9]: The key idea is to aggregate the badwidth of earby users to help a particular mobile video user s streamig. However, the BA model maily focused o the simple oe-to-may cooperatio betwee a sigle video user ad multiple helpers. We cosider a more geeral may-to-may cooperatio framework with multiple video users ad multiple helpers, where each user acts as both the video user ad the helper. ) DD Model [] [3]: The key idea is to eable earby video users to share their dowloaded video segmets with each other through DD liks. I [], Golrezaei et al. studied the cache-based DD cooperatio, where mobile video users cache popular video cotets ad deliver to other users via DD liks i the future. Our model differs from that of [] i the followig aspects. First, we cosider the real-time cooperatio of earby users, while [] cosidered the future opportuistic cooperatio. Secod, we study the joitly video streamig of multiple users, while [] studied the video streamig of differet users separately. I [] [3], researchers studied the real-time DD based cooperatio, where multiple earby users watch the same video ad share video cotets cooperatively via DD liks. Our model is similar but more geeral tha those i [] [3], as we allow differet users to watch differet videos. This itroduces a additioal dimesio (i.e., video idex) whe makig the schedulig decisio, hece ivolves additioal challeges. 3) CMS Model [4] [6]: Our proposed cooperative streamig model falls ito this category. The key idea is to eable earby mobile video users pool their etwork resources together to satisfy all users video streamig requiremets joitly. I [4], Pu et al. proposed a rate adaptatio algorithm for optimizig the adaptive streamig across multiple mobile users (possibly watchig differet videos), but they did t cosider the idividual characteristics of differet users. I [5], Georgopoulos et al. studied the CMS model i a home etworkig sceario, but they did t cosider the importat etwork characteristics such as the trasmissio rate. I [6], Tag et al. focused o the icetive desig i the multi-user CMS model ad proposed a multi-dimesioal auctio-based mechaism to icetivize video users to collaborate with each other uder iformatio asymmetry. However, they either performed the performace boud aalysis, or desiged the olie algorithm. I this work, we will study both the theoretical performace boud ad the practical olie algorithm systematically. 4. Please refer to our survey paper [3] for a comprehesive review.

4 4) MPP Model [7] [9]: The key idea is to eable video users act as virtual video servers ad sed the dowloaded segmets to other users via Iteret. Thus, i the MPP model, a video user ca potetially help other users that are ot physically close-by. The key differece betwee our model ad the MPP model is as follows. I the MPP model, each video segmet has multiple copies residig o both the video server ad the user devices (peers), ad video users ca dowload a video segmet from either the server or a user peer, via his ow wireless cellular lik. Hece, the key desig purpose of MPP model is to reduce the load of the video server. I our cooperative streamig model, however, each video segmet has a uique copy residig o the video server, ad users ca dowload a video segmet (from the video server) either via his ow wireless cellular lik or a eighbor s cellular lik. Hece, the key desig purpose of our model is to reduce the ucertaity or improve the efficiecy of user s wireless cellular lik. 3 SYSTEM MODEL 3. Network Model We cosider a set N, {,...,N} of mobile video users i wireless cellular etworks, who wat to watch videos (o their smartphoes) via 3G/4G cellular liks. Mobile users are heterogeeous i terms of their cellular lik capacities ad video quality requiremets. For example, a user requestig a high quality video may suffer from a low cellular lik capacity, due to factors such as a severe chael fadig ad a high cellular etwork cogestio. This may reduce the quality of the video ad icrease the video quality variatio, both harmig the user s quality of experiece (QoE). O the other had, a user requestig a low quality video (or ot playig a video at all) may experiece a high cellular lik capacity, ad have extra capacity to help other users. Thus, it is desirable to eable users to coect with each other to dowload the streamig video cotets cooperatively. There are may real-world applicatio scearios for such a cooperative video streamig. Cosider, for example, that a group of frieds who wat to watch a live soccer match together o their phoes at a remote locatio (e.g., a campig or skiig site), or a family who wats to watch oe or multiple movies o their phoes i the trai or i the car, or a group of studets who wat to watch differet olie lectures usig WiFi at a busy hotspot (e.g., a classroom). I all these cases, some or all of the users may have poor or itermittet cellular coectivity, depedig o the coverage of their service providers. Thus, aggregatig the resources of earby users for the cooperative video streamig may sigificatly improve the overall user satisfactios. ) User-Provided Network (UPN): UPN eables earby mobile users to form a cooperative group (via WiFi) ad aggregate their radio coectios ad resources for cooperative data dowloadig. We cosider a geeral multi-user cooperative streamig scheme based User : :3 Hotspot :3 3:5 Hotspot Figure. Hotspot-Based Mobility Model. User 's Movig Path o UPN. Namely, i a cooperative group, each user ca dowload video data for other users usig his ow cellular lik (ad resources) ad dowload his video data through other users liks (ad resources). As metioed previously, we assume that some well-desiged icetive mechaisms (e.g., Nash bargaiig [3], auctio [3] [34], or others trust mechaisms [35] [38]) have bee adopted, such that users are willig to participate i the cooperative streamig system to help others. Figure illustrates such a cooperative streamig model with three users {,, 3}, where user dowloads oe segmet for himself, oe segmet for user, ad two segmets for user 3, while user dowloads two segmets for himself ad oe segmet for user 3. Note that user 3 does ot dowload ay video cotet due to the temporary iterruptio of his cellular lik. ) Mobility Model: The cooperatio gai of such a cooperative streamig highly depeds o the umber of cooperative users ad the duratio of cooperatio, both closely related to the users mobility patters. We adopt a hotspot-based mobility model [4], where the whole area is divided ito a set of small hotspots ad the o-hotspot area, 5 ad each user moves across a sequece of hotspots durig his travel i the followig patter: stayig for a certai period of time i each hotspot that he passes, ad takig some time for each trasitio (from oe hotspot to aother). Figure illustrates such a mobility model, where user stays at hotspot for 3 miutes (: :3), ad the takes hour to move to hotspot ad stays at hotspot for 45 miutes (:3 3:5). I such a hotspot-based mobility model, users i the same hotspot at the same time ca coect with each other (hece form a cooperative group), while users i differet hotspots or i the o-hotspot area caot. Such a mobility model has bee widely-used i the scearios where users eed to take certai time to iteract with each other (e.g., mobile data forwardig i [4]). Notatios: We cosider the operatio i a period of cotiuous time T, [, T], where t =is the iitial time ad T is the edig time. Let A, {,...,A} deote the set of all hotspots, ad {} deote the o-hotspot area. The key otatios i this part are listed below. a (t) A S {}: the locatio of user at time t; h (t) > : the cellular lik capacity of user at time t; e,m (t) {, }: the idicator deotig whether users ad m are ecoutered (i.e., i the same hotspot) at time t, i.e., e,m (t) =if a (t) =a m (t) A. 5. A hotspot is a small area where users are likely to stay for a substatial amout of time (e.g., a bus stop or a coffee shop), hece ca maitai their WiFi coectios for a reasoable amout of time. 4

5 5 For coveiece, we refer to the user locatio ad cellular lik capacity {(a (t),h (t)), 8 N,t T}as the etwork iformatio, which varies radomly over time. Note that the ecouter idicator e,m (t) ca be derived from the locatio iformatio of users ad m. 3. Video Streamig Model We cosider a typical ABR streamig model [], where a sigle source video file is partitioed ito multiple segmets ad delivered to a video user usig HTTP. The key features of ABR model are summarized below. (i) Video Segmetig: To facilitate the video delivery over the Iteret, a source video file is divided ito a sequece of small HTTP-based file segmets, each cotaiig a short iterval of playback time (e.g., secods) of the source video, which is possibly several hours i term of the total duratio (e.g., a movie). A user dowloads the video segmet by segmet. (ii) Multi-Bitrate Ecodig: Each segmet is ecoded at multiple bitrates, each correspodig to a specific video quality (such as resolutio). A user ca select differet bitrates for differet segmets accordig to real-time etwork coditios. (iii) Data Bufferig: For smoothly playig, each dowloaded segmet is first stored i a buffer at the user s device, ad the fetched to the video player sequetially for playback. The maximum buffer size o user device is usually limited (e.g., 4 Secods). Notatios: Key otatios i this part are listed below. > : segmet legth (i secods) of user s video; R, {R,R,...,R Z } (with <R <R <...< R Z ): the set of bitrates (i Mbps) available for user, which depeds o both the sever-side protocols ad the user-side parameters such as device type. Q > : maximum buffer size (i secods) of user. with each elemet s [k] deotig the iformatio of the k-th dowloaded segmet, icludig the segmet ower u, bitrate level z, bitrate r = Ru, z 6 dowload start time t s, ad ed time t e. Namely, we ca write s [k] as s [k] =(u, z, r, [t s,t e ]). To distiguish the iformatio of differet segmets, we will also write the iformatio of segmet s [k] as (u [k],z [k],r [k],t s [k],te [k] ) wheever eeded. It is easy to see that our cooperative streamig model geeralizes the model without crowdsourcig, i which case we ca simply restrict each user dowloadig oly his ow segmet, i.e., u [k] =, 8, k. Next we provide the costraits for a feasible dowloadig sequece S of user. (i) Timig Costrait: As users dowload segmet by segmet, we have the followig timig costrait: C. : t e [k] apple ts [k+], 8k =,..., S. A strict iequality implies that user waits for some time before startig to dowload the ext segmet s [k+], for example, whe all users buffers are full. (ii) Capacity Costrait: Each segmet s [k] =(u, z, r, [t s,t e ]) cosists of r u Mbits of video data, ad is dowloaded by user withi time iterval t s [k] [k],te. Hece, we have the followig cellular lik capacity costrait: C. : r u apple Z t e [k] t s [k] h (t)dt, 8k =,..., S, where h (t) is the real time cellular lik capacity (i Mbps) of user at time t. (iii) Ecouter Costrait: Each user ca oly dowload data for a earby ecoutered user. Hece, a segmet with s [k] =(u, z, r, [t s,t e ]), 6= u is feasible oly if users ad u are ecoutered durig t s [k], [k] te, i.e., C.3 : e,u (t) =, t h t s [k], te [k] i, 8k =,..., S. 4 PROBLEM FORMULATION I this sectio, we first characterize the users behaviours i the cooperative streamig model, ad the formulate the associated optimizatio problem. Specifically, with the ABR streamig, each source video is dowloaded segmet by segmet. Namely, each user starts to dowload a ew segmet (with a specific bitrate) oly whe completig the existig segmet dowloadig. Hece, users operate i a asychroous maer, as they may complete segmet dowloadig at differet times. We refer to such a operatio scheme as the segmeted dowload operatio. 4. Dowloadig Sequece With the segmeted operatio, each user s dowloadig operatio ca be characterized by a sequece: S, s [], s [],..., s [k],..., () 4. Receivig Sequece Give the feasible dowloadig sequeces of all users, i.e., S, 8 N, we ca derive the segmet receivig sequece of each user m as follows: 7 [ bs m = s [k]. () N,k{,..., S }:u [k] =m We assume that a proper dowload schedulig has bee adopted, such that there is o repeated segmets withi bs m, ad all segmets i S b m are sorted accordig to the playback order. We deote the k-th segmet i the 6. Here the bitrate r = R z u is redudat iformatio, ad maily itroduced for facilitatig the later descriptio. 7. We assume the WiFi trasmissio time is zero. Oe motivatio for such a assumptio is that the recet IEEE 8. stadard family (e.g., 8., ac, ad) has become icreasigly powerful, ad ca support a data rate up to Gbit/s, which is much higher tha those of the curret cellular systems (such as 3G ad 4G).

6 6 reordered b S m by ŝ m[k]. The, we ca write the receivig sequece of user m as: bs m, ŝ m[], ŝ m[],..., ŝ m[k],..., (3) with each elemet ŝ m[k] = û, ẑ, ˆr, [ˆt s, ˆt e ] deotig the iformatio of the k-th segmet played by user m. Similarly, we will write the iformatio of ŝ m[k] as (û m[k], ẑ m[k], ˆr m[k], ˆt s m[k], ˆt e m[k] ) wheever eeded. It is easy to see that û m[k] = m for all ŝ m[k] S b m. To facilitate the later aalysis, we further assume that ˆt e m[k] apple ˆt e m[k+], 8k =,..., b S m, that is, user m receives the segmets i S b m sequetially. Note that this ca always be achieved by a proper schedule of dowloadig sequeces with the full etwork iformatio. For example, if ˆt e m[k] > ˆt e m[k+], i.e., the k+-th segmet is received before the k-th segmet, we ca simply chage their dowloadig orders. As metioed previously, each received segmet is stored i a buffer at the user s device, ad the is fetched to the video player sequetially for playback. Let q m[k] deote the buffer level (i secods) of user m whe receivig the k-th segmet, i.e., at the time ˆt e m[k]. The, we have the followig buffer update rule for user m: h + q m[k] = q m[k ] ˆt e m[k] ˆt e m[k ] i + m, (4) ˆt e m[k where [x] + = max{,x}. Here ˆt e m[k] ] is the time iterval betwee receivig of ŝ m [k ] ad ŝ m[k], durig which a period ˆt e m[k] ˆt e m[k ] of video is played back ad removed from the buffer; m is the segmet legth (playback time) of the ewly received segmet ŝ m[k]. Sice each user m s buffer size is limited with Q m (secods), we have the followig buffer costrait: C.4 : apple q m[k] apple Q m, 8k =,..., b S m. 4.3 User Welfare The welfare of a user maily cosists of two parts: a utility fuctio capturig the user s QoE for video service, ad a cost fuctio capturig the user s eergy cosumptio for both video dowloadig ad playig. ) Quality-of-Experiece (QoE): Users ofte desire for a higher video quality without frequet quality chages ad freezes durig playback. Hece, a user s QoE maily depeds o the video quality, quality fluctuatio, ad rebufferig. Note that bitrate is a good measuremet of video quality, ad i geeral there is a distict ad mootoic relatioship betwee bitrate ad quality. Hece, we will defie the QoE o bitrate for otatioal coveiece. (i) Video Quality: A higher video quality (bitrate) brigs a higher value for users. Let g (r) deote the value that user achieves from bitrate r durig oe uit of playback time. The, the total value that user achieves from all received segmets S b (each with a playback time of û [k] = ) is: b S V ( S b X ), g ˆr [k]. (5) k= Obviously, g ( ) is a icreasig fuctio (as video quality mootoically icreases with bitrate). I our simulatios, we adopt the followig value fuctio: g (r) = log( + r), where > is a user-specific evaluatio factor capturig user s desire for a high quality video service. (ii) Quality Fluctuatio: The chage of quality (bitrate) durig playback decreases the user QoE, especially whe the quality is degraded. I this work, we assume that there is a value loss proportioal to the bitrate decrease oce the quality is degraded, while there is o value loss QDEG whe the quality is upgraded. Let > deote the value loss of user for oe uit (i Mbps) of bitrate decrease. The, the total value loss of user iduced by quality degradatio is 8 L QDEG ( S b SP b QDEG ), ˆr + [k ] ˆr [k], (6) k= where [x] + = max{,x}. Here ˆr [k ] > ˆr [k] idicates that a quality degradatio occurs betwee ŝ [k ] ad ŝ [k], with a bitrate decrease of ˆr [k ] ˆr [k]. (iii) Rebufferig: If a video buffer is exhausted before receivig a ew segmet, the video player has to freeze the playback ad rebuffer the video for a certai time. Such a freeze durig playback is called rebufferig. The rebufferig durig playback greatly affects the user QoE. By the buffer update rule (4), a rebufferig occurs whe q [k ] < ˆt e [k] ˆt e [k ], with a detailed rebufferig time ˆt e [k] ˆt e [k ] q [k ]. REBUF Let > deote the value loss of user for oe uit (secod) of rebufferig time. The, the total value loss of user iduced by video rebufferig is L REBUF ( S b SP b ), k= REBUF hˆt e [k] ˆt e [k ] q [k ] i +. (7) Based o the above, we ca defie the utility of each user uder a receivig sequece b S as follows: U ( S b ), V ( S b ) L QDEG ( S b ) L REBUF ( S b ). (8) ) Eergy Cost: Users icur some eergy cost i video streamig. Such eergy cost maily icludes the eergy cosumptios for dowloadig data via cellular liks (ad Iteret) ad exchagig data via WiFi liks. (i) Eergy Cosumptio for Video Dowloadig (via Celluar ad Iteret): Whe dowloadig data via the cellular lik (ad Iteret), users eergy cosumptio depeds o both the dowloadig time ad the dowloaded data volume [4]. Let c TIME deote the timerelated eergy cosumptio factor of user (i.e., for 8. Our model ca be directly exteded to the case with upgrade loss, by simply chagig [x] + ito the absolute operatio x.

7 7 each uit of dowloadig time), ad c DATA deote the volume-related eergy cosumptio factor of user (i.e., for each uit of dowloaded data). The, the eergy cosumptio of user for dowloadig video cotets via cellular liks ad Iteret is [4]: SP E CELL (S ), c TIME (t e [k] t s [k] )+cdata r [k] u [k]. k= (9) (ii) Eergy Cosumptio for Video Exchagig (via WiFi): Whe dowloadig a segmet for others, the user eeds to trasmit the data to the segmet ower via local WiFi lik, the eergy cosumptio of which also depeds o the trasmittig time ad the trasmitted data volume [4]. Let w TIME ad w DATA deote the time-related ad volume-related eergy cosumptio factors of user o the WiFi lik, respectively. The eergy cosumptio of user for video exchagig o WiFi lik is [4]: SP E WIFI (S ), k= w TIME +w DATA r [k] u [k] () (u [k] 6= ), where (u [k] 6= ) =if u [k] 6= (i.e., the segmet s [k] is dowloaded for others), ad (u [k] 6= ) =otherwise. Here we have assumed that the WiFi trasmissio time of a sigle segmet is small ad hece egligible. Based o the above, we ca derive the total eergy cosumptio of each user uder a dowloadig sequece S ad receivig sequece S b as follows: C (S, b S ), E CELL (S )+E WIFI (S ). () Note that our proposed system ca work with other eergy models (e.g., those i [43]). I fact, the eergy cosumptio modelig ad eergy savig are ot the key objective of the proposed system. Istead, our key objective is to improve the QoE of users. 3) Welfare: The welfare of each user, deoted by P, is defied as the differece betwee the utility (capturig the QoE of users) ad the cost (capturig the eergy cosumptio), i.e., P (S, S b ), U ( S b ) C (S, S b ). () The social welfare is the aggregate welfare of all users: P W (S,...,S N ), N P (S, S b ), (3) = where the receivig sequece b S of each user ca be derived from the dowloadig sequeces S,N. 4.4 Problem Formulatio Our purpose is to fid the proper dowload schedulig to maximize the social welfare achieved i the proposed cooperative streamig model. First, i a ideal sceario with the complete etwork iformatio, we ca formulate the followig offlie social welfare maximizatio problem: max W (S,...,S N ), {S,N } (4) s.t. C. C.4. To solve this offlie optimizatio problem, we eed to kow the complete etwork iformatio. The solutio of (4), deoted by W, provides the theoretical performace boud (i term of social welfare) of the proposed cooperative streamig system. Note that (4) is a MILP (mixed iteger liear programmig) ad challegig to solve. 9 Hece, we will derive a feasible upper-boud ad a feasible lower-boud of W i Sectio 5. Secod, i a more geeral sceario without complete (future) etwork iformatio, we eed to desig olie schedulig algorithms, where the dowloadig operatio of each user is performed i a olie ad distributed maer. We will study such a olie schedulig algorithm desig ad the associated performace evaluatio i Sectio 6. 5 PERFORMANCE BOUND ANALYSIS I this sectio, we study the theoretical social welfare performace boud of the proposed cooperative system (i.e., the solutio of the offlie social welfare maximizatio problem (4)), which serves as a bechmark for the olie schedulig solutios i Sectio 6. However, directly solvig (4) is challegig due to the followig reasos. First, users operate i a asychroous maer. Namely, differet users may start to dowload ew segmets at differet times. Secod, (4) ivolves both discrete variables (e.g., u ad z) ad cotiuous variables (e.g., t s ad t e ), hece is a complicated mixed-itegral optimizatio problem. Third, (4) ivolves the itegral operatio (C.), which is eve more challegig. Hece, we will focus o fidig upperboud ad lower-boud for the desired performace boud of the cooperative streamig system. To achieve this, we propose a virtual time-slotted dowload operatio scheme, uder which the problem ca be formulated as a liear programmig, hece ca be solved by may classic methods. We will show that the solutio of (4) uder the segmeted operatio scheme (i.e., the theoretical performace boud of the proposed cooperative streamig system) is bouded by the solutios uder this virtual time-slotted system. 5. Time-Slotted Dowload Operatio To model the time-slotted operatio scheme, we divide the whole time period [,T] ito multiple time slots, each with the same legth. For coveiece, we ormalize the legth of each slot to be oe. Hece, there is a set of T time slots, deoted by T = {,,...,T}, with the -th slot correspodig to time iterval [, ]. 9. This is because i each user s dowloadig sequece, we eed to determie ot oly the order of segmets, but also the dowload start time ad ed time for each segmet. Eve for the simplest case with a sigle user, it is still a NP-hard problem to optimally determie the dowload start time ad ed time of each segmet.. It is importat to ote that this time-slotted operatio scheme is oly used for characterizig the theoretical performace boud, but ot for the practical implemetatio.

8 8 User User Segmeted Operatio (Asychroous) Time User 's data Time-Slotted Operatio (Sychroous) Slot Slot Figure 3. Segmeted vs Time-Slotted Operatio. User 's data Slot 3 Time Uder the time-slotted operatio scheme, each video is dowloaded slot by slot i a sychroized maer, rather tha segmet by segmet uder the segmeted operatio. Thus, i this case, we ca focus o the segmets that each user dowloads i each time slot, istead of the segmet dowloadig sequece. Moreover, to guaratee the sychroous operatio, we require that each segmet must be completely dowloaded withi oe time slot. Namely, users caot dowload a segmet across multiple time slots. For clarity, we illustrate the differece (i dowload schedulig) betwee the segmeted operatio ad the time-slotted operatio i Figure 3, where blue blocks deote user s data ad orage blocks deote user s data. Uder the segmeted operatio (left), users start to dowload data at differet times, while uder the timeslotted operatio (right), users are sychroized, ad dowload data at the begiig of each time slot. ) Dowloadig Vector: With the time-slotted operatio, the dowloadig operatio of each user ca be characterized by a dowloadig vector: K, apple z,m( ), 8 T,mN,z {,...,Z}, (5) where each elemet apple z,m( ) is a o-egative iteger, deotig the total umber of segmets with bitrate level z that user dowloads for user m i time slot. Give the dowloadig vector K, we ca derive the total data that user dowloads i each time slot : x DL ( ) = N P m= where x,m ( ), x,m ( ) = Z P z= N P m= z= ZP apple z,m( ) m Rm, z (6) apple z,m( ) m R z m is the amout of data for user m i slot t. The, we ca defie the lik capacity costrait ad ecouter costrait for a feasible dowloadig vector K : ec. : x DL ( ) apple H ( ), ec.3 : e,m (t) =,t [, ], if x,m ( ) >, where H ( ) = R h (t)dt is the total cellular lik capacity of user i time slot. Note that here we do ot eed to cosider the timig costrait (C.) as the operatio is already slot by slot. ) Receivig Vector: Give feasible dowloadig vectors of all users, i.e., K, 8 N, we ca derive the total playback time that user m receives i each time slot : P ym RE ( ) = N P y,m ( ) = N ZP apple z,m( ) m, (7) = = z= where y,m ( ), Z P z= apple z,m( ) m is the total playback time that user m receives from user i slot. Let q m ( ) deote the buffer level (i secods) of user m at the ed of time slot. The, we have the followig buffer update rule for user m: q m ( ) =[q m ( ) ] + + ym RE ( ), (8) where [x] + = max{,x}. Here oe time uit of video is played back durig time slot, ad ym RE ( ) is the playback time of the ewly received segmets i slot. Similarly, we have the followig buffer costrait: ec.4 : apple q m ( ) apple Q m, 8 =,...,T. 3) User Welfare: Now we defie the user welfare ad social welfare uder the time-slotted operatio. (i) Video Quality: Similar as (5), the value that user achieves from all received segmets is: P ev, T NP ZP apple z m,( ) g (R). z (9) = m= z= (ii) Quality Fluctuatio: Without loss of geerality, we assume that all the received segmets of each user i each time slot are sorted i ascedig order of bitrate. Hece, quality degradatio oly occurs betwee two successive time slots, while ever occurs withi a time slot. Let r H ( ) ad r L ( ) deote the highest bitrate ad lowest bitrate that user receives i slot. The, similar as (6), the value loss of user due to quality degradatio is: el QDEG, T P = QDEG [r( H ) r( )] L +. () (iii) Rebufferig: By the buffer update rule i (8), a rebufferig occurs i time slot whe q m ( ) <, with a rebufferig time q m ( ). The, similar as (7), the value loss of user iduced by rebufferig is el REBUF, T P = REBUF [ q m ( )] +. () (iv) Eergy Cosumptio for Video Dowloadig (via Cellular ad Iteret): Similar as (9), the eergy cosumptio of user for dowloadig video is ee CELL, T P = c TIME xdl ( ) H + ( ) cdata x DL ( ), () where xdl ( ) H ( ) is the actual dowloadig time i slot. (v) Eergy Cosumptio for Video Exchagig (via WiFi): Similar as (), the eergy cosumptio of user for exchagig video o local WiFi liks is ee WIFI, T P NP = m=,m6= (w TIME +w DATA x,m ( )). (3) Based o the above, the welfare of each user is ep (K,...,K N ), V e L eqdeg el REBUF ee CELL ee WIFI. (4). If ot, we ca simply chage the orders of related segmets.

9 9 4) Problem Formulatio uder Time-Slotted Operatio: Now we ca defie the social welfare maximizatio problem uder the time-slotted dowload operatio: P fw, N ep (K,...,K N ), max {K,N } = (5) s.t. C. e C.4. e Similar to (4), this is a offlie optimizatio problem ad requires the complete etwork iformatio. Moreover, (5) is a iteger programmig, ad ca be solved by may classic methods. Hece, we skip the detailed derivatios. For otatio coveiece, we deote the solutio of (5) by f W. 5. Performace Boud Now we characterize the theoretical performace boud W of the proposed cooperative streamig system (uder the segmeted operatio) by usig the solutio W f of (5) uder the virtual time-slotted operatio. For coveiece, we deote, (,..., N ) as the vector cosistig of all users segmet legths, ad deote W( ) ad W f ( ) as the solutios of (4) ad (5) uder, respectively. We refer to a vector as a iteger multiple of aother vector, if each elemet i is a iteger multiple of the correspodig elemet i. For example, =(,...,N) is a iteger multiple of =(.5,...,N/). Propositio. If is a iteger multiple of, the W( ) apple W ( ), ad W f ( ) apple W f ( ). This propositio ca be proved by showig that i both schemes, ay dowloadig operatio uder ca be equivaletly achieved uder. Propositio. If! (i.e.,!, 8 N), the W( ) = W f ( ). This propositio ca be proved by showig that with ifiitely small segmet legths!, ay dowloadig operatio uder the time-slotted operatio scheme ca be equivaletly achieved uder the segmeted operatio scheme, ad vise versa. Propositio 3. If is a fiite vector (i.e., each elemet is a fiite umber), the W( ) fw ( ). This propositio ca be proved by showig that with fiite segmet legths, ay dowloadig operatio uder the time-slotted operatio scheme ca be equivaletly achieved uder the segmeted operatio scheme, but ot vise versa. Based o the above, we have the followig theorem. Theorem. Give a segmet legth, the theoretical performace upperboud W( ) is bouded by: fw ( ) apple W ( ) apple W f (!). Ituitively, this theorem states that with ay, the theoretical performace boud W( ) of our proposed cooperative streamig system is (a) lower-bouded by fw ( ) (i.e., the optimal performace of the virtual timeslotted system with the same segmet legth vector ), ad (b) upper-bouded by W f (!) (i.e., the optimal performace of the virtual time-slotted system with ifiitely small segmet legths! ). Therefore, the performace of the virtual time-slotted system uder differet characterizes the theoretical performace regio of our proposed cooperative streamig system. 6 ONLINE SCHEDULING ALGORITHMS I the previous sectio, we have aalyzed the theoretical performace boud of the cooperative streamig system, which is achievable i a ideal sceario with complete etwork iformatio. I practice, however, etwork chages radomly over time, ad hece it is difficult to obtai the future ad global etwork iformatio. I this sectio, we study the practical sceario where the future ad global etwork iformatio is ot available. We propose a olie schedulig algorithm based o the Lyapuov optimizatio framework [3], which relies oly o the curret local etwork iformatio ad the schedulig history, while ot o ay future or global etwork iformatio. 6. Olie vs Offlie We first discuss the key differece betwee olie schedulig ad offlie schedulig. I the offlie schedulig, the segmet dowloadig sequeces of all users at all time are determied i advace, through, for example, the offlie social welfare maximizatio problem (4), which requires the complete etwork iformatio. I the olie schedulig, however, each user makes the dowload schedulig decisio (regardig the ext segmet to be dowloaded) i real time, e.g., at the time whe he completes a previous segmet dowloadig. I our proposed cooperative streamig system, such a real time dowloadig decisio maily icludes two problems: whose segmet to be dowloaded, ad at which bitrate level? The decisio may deped o differet criteria such as the real time user buffer levels (e.g., i [6]), the chael badwidth or throughput predictios (e.g., i [7]), ad other specific objective fuctios (e.g., Lyapuov drift-plus-pealty described below). 6. Lyapuov-Based Olie Schedulig Lyapuov optimizatio [3] is a widely used techique for solvig stochastic optimizatio problems with time average costraits. I our model, a implicit time average costrait is that the average segmet arrivig rate should be same as the video playback rate i term of segmet. If the video playback rate is smaller, the the. For example, for a video with -secod segmet, the playback rate i term of segmet is.5 (segmets per secod).

10 dowloaded segmets will be frequetly dropped due to the limited buffer size; if the video playback rate is larger, the the rebufferig will frequetly happe. Both cases are ot desirable i this system. To this ed, we itroduce the Lyapuov optimizatio techique to optimize the dowloadig schedulig i a olie maer. Suppose that a user completes a segmet dowloadig at time t, ad eeds to make the dowloadig decisio regardig the ext segmet to be dowloaded. We deote such a decisio by (u, z), where u N is the ower of the segmet to be dowloaded, ad z {,...,Z} is the bitrate level of the segmet to be dowloaded. Obviously, a feasible decisio (u, z) of user at time t satisfies the followig user ecouter costrait: e,u (t) =. For aalytical coveiece, we further deote q m (t) as the buffer level of each user m at time t, ad deote r m as the bitrate of user m s last received segmet. This iformatio captures the curret etwork state ad historical schedulig iformatio that ca be observed. ) Objective Fuctio: Give a feasible decisio (u, z) of user, the data volume to be dowloaded is Ru z u (Mbit), ad the estimated dowloadig time is u,z, Ru z u h (t).3 The total eergy cosumptio of user (for this particular dowloadig operatio) ad user u (for playig the dowloaded segmet) is: C (u, z) =E CELL + E WIFI. The utility of receiver u o this particular segmet is U u (u, z) =V u Lu QDEG Lu REBUF. The utility of other user m 6= u due to this operatio is U m (u, z) = Lm REBUF REBUF = m [ u,z q m (t)] +, which oly icludes the potetial rebufferig loss. Therefore, the total welfare geerated uder (u, z) is P (u, z), N P m= U m (u, z) C (u, z). (6) ) Lyapuov Drift: Followig the Lyapuov framework, we defie a modified Lyapuov fuctio: NP J(t), [Q m q m (t)]. (7) m= The Lyapuov drift is the chage of Lyapuov fuctio (from oe decisio-makig time to the ext), i.e., (t), J(t + u,z ) J(t), NP [Q m q m (t + u,z )] [Q m q m (t)], = m= (8) where q m (t + u,z ) is the estimated buffer level of user m at time t + u,z (i.e., the ext decisio-makig time of user ). For the receiver u, the estimated buffer level is: q u (t + u,z )=mi{q u, [q u (t) u,z] + + u }. 3. Here we use the curret chael capacity h (t) to approximate the capacity i a period of future time. Note that the actual dowloadig time may be differet from u,z due to the chael stochastics. Algorithm : Lyapuov-based Olie Schedulig while at each decisio-makig time t do if q (t)+ >Q, 8 N the /* o buffer ca afford oe more segmet */ Wait for T w =mi N (q (t)+ Q ) secods; else Dowload a segmet of bitrate level z for user u : (u,z ) = arg mi u,z (t), (t) P (u, z) For other user m 6= u, the estimated buffer level is: q m (t + u,z )=[q m (t) u,z] +. 3) Olie Schedulig Algorithm: By the Lyapuov optimizatio theorem, to stabilize the system while optimizig the objective, we ca use such a schedulig policy that greedily miimizes drift-plus-pealty: (t), (t) P (u, z), (9) where the egative welfare ( P (u, z)) is viewed as the pealty icurred at time t, ad is a cotrol parameter. It is importat to ote that the buffer levels (appearig i (t)) serve as regulatio factors, such that the user with a larger idle buffer ca be more likely to be scheduled (hece reducig the possibility of rebufferig). This term is differet from the rebufferig loss i (7), which is the actually realized loss whe a rebufferig evet actually happes. Based o the above aalysis, we ow desig a olie algorithm that aims at miimizig the drift-pluspealty (9) i each decisio-makig time. We preset the detailed algorithm i Algorithm. Note that a user may decide ot to dowload ay segmet at a decisiomakig time, whe, for example, all buffers are full ad caot afford oe more segmet. I this case, the user will wait for a certai time ad the trigger decisiomakig evet agai. Hece, a decisio-makig time ca be either the time that a user completes a segmet dowloadig or the time that a user is triggered by the waitig timer. Note that the olie schedulig i Algorithm works i a distributed maer, as each user makes the decisio idepedetly. To coordiate the dowloadig decisios of differet users ad to avoid the redudat dowloadig of the same segmet, earby users eed to exchage the cotext iformatio (e.g., buffer legth, segmet size, ecode bitrate, ad url). To illustrate this, we costruct a real demo system o Raspberry PI. Please refer to [49] for more details. 4) Performace Aalysis: Now we aalyze the performace of Algorithm. Let t [k] deote the k-th decisiomakig time (coutig all users), ad let P [k] deote the associated welfare achieved i the k-th dowload operatio. The, the social welfare geerated by Algorithm

11 durig the whole time [,T] ca be computed by: W( ) = P P [k]. t [k] applet By the Lyapuov optimizatio theorem (Theorem 4. i [45]), we obtai the followig gap for W( ) ad W ( ), i.e., the theoretical performace upperboud. Theorem. i h i lim hw E ( ) E W B ( ), T! where E[.] is expectatio, ad B is a positive costat. Theorem shows that Algorithm coverges to the theoretical performace boud W( ) asymptotically, with a cotrollable approximatio error boud O( ). However, this theorem does ot directly help us calculate the actual gap betwee W( ) ad W ( ) i a particular experimetal sceario, as T is fiite i practice. To this ed, we propose aother approach based o Theorem for the practical calculatio of the actual gap, i.e., W ( ) W ( ) apple f W (!) W ( ). Note that W f (!) is the solutio of the iteger programmig problem (5), ad ca be easily computed i a practical experimet after collectig the complete etwork iformatio. I our experimets, the average gap betwee W( ) ad W f (!) is smaller tha 3%. 7 EXPERIMENTS AND PERFORMANCE 7. Experimet Settig ) Datasets: To evaluate the realistic performace of the proposed cooperative streamig system, we coduct experimets based o real data traces from two datasets: ISF [46] ad NWF [47]. 4 Both datasets record the user access sessios at a set of WiFi hotspots i differet coutries durig a log period of time (3 years for ISF ad 5 moths for NWF), represetig two differet (hotspot-based) mobility scearios: users ecouter more frequetly i NWF, while the duratio of each ecouter is larger i ISF. To simulate the video watchig behaviours of mobile users ad the real cellular lik throughputs for video streamig, we use the video viewig sessio logs obtaied from BestTV [48], oe of the largest OTT (Over The Top) video service providers i Chia. There are 5 differet bitrate levels (for mobile users) i this dataset: {.,.4,.7,.3,.3}Mbps, correspodig to the lowest to the highest video resolutios, respectively. Based o the segmet legth, bitrate, ad dowloadig time, we ca calculate the measured ed-to-ed lik throughput for each segmet dowloadig. We use this measured throughput to approximate the cellular lik capacity 4. ISF is provided by a o-profit orgaizatio Ile Sas Fil i Caada, ad is ope source (available at CRAWDAD [46]). NWF is obtaied from a wireless service provider NextWiFi i Chia [47]. i our experimets. Moreover, the eergy cosumptio factors are chose accordig to the real measuremet give i [4], [44]. ) Existig Olie Algorithms: To evaluate the performace of our proposed Lyapuov-based olie algorithm, we also perform simulatios usig the followig two typical existig olie algorithms: Buffer-based algorithm [6] ad Chael Predictio-based algorithm [7]. Specifically, buffer-based algorithm [6] itroduces a liear mappig betwee buffer ad bitrate, ad selects the ext segmet bitrate based o the curret buffer level: a higher buffer level is mapped to a higher bitrate. Chael predictio-based algorithm [7] proposes a chael predictio method, ad selects the ext segmet bitrate based o the predicted chael capacity: the highest bitrate that ca be supported by the predicted chael capacity Multiple-User Case Now we perform experimets for the multi-user sceario, where some users play videos (called video users), while others remai idle ad ca potetially help the ecoutered video users. 6 For simplicity, we assume that all video users play the high-resolutio videos (bitrate.3mbps). The total video legth is 5 secods, the segmet legth is secods, ad the maximum buffer legth at the user s device is 4 secods. We use these multi-user experimets to illustrate both the cooperatio gai of the proposed cooperative streamig system ad the performace gai of the proposed algorithm. I the followig experimets, we cosider a total of 5 users ad radomly choose a subset of users as video users. We cosider differet etwork coditios, characterized by the rage of the average lik capacity. For example, a bad etwork coditio correspods to a rage [,.7]Mbps, uder which each user will be radomly assiged by a real data trace with a average lik capacity smaller tha.7mbps. ) Average Bitrate: Figure 4 shows the average bitrates with differet percetages of video users uder differet etwork coditios. For each video user percetage ad 5. Note that both algorithms i [6] ad [7] were desiged for the sigle-user sceario, ad cosidered oly the bitrate adaptatio. I the multi-user sceario, we eed to cosider both bitrate adaptatio ad segmet ower selectio (i.e., whose segmet to be dowloaded) as discussed i Sectio 6. To this ed, we itroduce the followig segmet ower selectio policy for these two algorithms i the multiuser sceario: Each user will choose to dowload the ext segmet for aother user u 6=, if ad oly if (i) q TH Q, (ii) q q u TH, ad (iii) q u =mi mn q m. Ituitively, user will choose to help the user with the lowest buffer level, if his ow buffer level is higher tha a ratio threshold TH ad meawhile is higher tha the lowest buffer level by a threshold TH. I our experimets, we will try differet values of TH ad TH, ad choose the best oes. 6. We also costruct experimets for the sigle-user sceario (i.e., o-cooperative sceario) to illustrate the performace gap of our proposed Lyapuov-based olie algorithm to the theoretical performace boud as well as to compare the bitrate adaptatio performace of our proposed algorithm with the existig olie algorithms. The detailed results are provided i [49].

12 Average Bitrate Multi-User Case (% Viewig Video) Lyapuov Predictio Buffer Full Cooperatio (A) ISF (B) NWF No Cooperatio ~.7 ~.3 ~.5 ~5. ~8. Average Lik Capacity (Mbps) Average Bitrate Multi-User Case (6% Viewig Video) ~.7 ~.3 ~.5 ~5. ~8. Average Lik Capacity (Mbps) Average Bitrate Multi-User Case (% Video Users) ~.7 ~.3 ~.5 ~5. ~8. Average Lik Capacity (Mbps) Average Bitrate Icrease (Mbps).5 Multi-User Case Lyapuov-Based Algorithm ~.7 ~.3 ~.5 ~5. ~8. Average Lik Capacity (Mbps) (a) (b) (c) (d) Figure 4. (a) Average Bitrate with % Video Users, (b) Average Bitrate with 6% Video Users, (c) Average Bitrate with % Video Users, (d) Average Bitrate Icrease uder the Lyapuov-based Algorithm. etwork coditio, we perform experimets with the three algorithms uder ISF ad NWF mobility traces, correspodig to differet ecouterig scearios (hece differet cooperatio probabilities). To fully characterize the cooperatio gai, we also ru the algorithms uder two bechmark ecouterig scearios: (i) a full cooperatio sceario, where all users are always ecoutered with each other, ad (ii) a o-cooperative sceario, where oe of users are ecoutered. Sugfigures (a) to (c) show the average bitrates with %, 6%, ad % video users, respectively. As illustrated i (a), the solid bar deotes the average bitrate uder the o-cooperative sceario, ad the hollow bar deotes the average bitrate uder the full cooperatio, i which the first (higher) lie deotes the average bitrate uder ISF (with a higher ecouterig probability) ad the secod (lower) lie deotes the average bitrate uder NWF (with a lower ecouterig probability). Subfigure (d) shows the average bitrate icrease (i.e., the cooperatio gai) usig our proposed Lyapuov-based algorithm, comparig with the achieved bitrate uder the o-cooperative sceario. The dash, solid, ad dashdot lies deote the results with %, 6%, ad % video users, respectively. The marks circle, square, ad triagle deote full cooperatio, ISF, ad NWF, respectively. From subfigure (a), we ca see that whe the percetage of (high-resolutio) video users is very high (e.g., %), the icrease of bitrate is very small uder a low lik capacity rage (e.g., lower tha.5mbps), as i this case all users are lack of capacity, hece obody ca help other users sigificatly. Uder a high lik capacity rage (e.g., [, 5]Mbps ad [, 8]Mbps), the icrease of bitrate becomes sigificat, as some users may have redudat capacities, hece ca help others. From subfigures (b) ad (c), we ca see that whe the percetage of video users is low (e.g., 6% or %), the bitrate icrease is sigificat uder all etwork coditios, maily due to the cotributios of the idle users. Subfigure (d) summarizes the icrease of bitrate uder our proposed algorithm. We ca see that with % video users, the icrease of bitrate cotiuously icreases with the lik capacity, as a larger capacity gives the video users more opportuities to obtai redudat capacity ad help others. With % video users, however, the icrease of bitrate cotiuously decreases with the lik % Video Users capacity, as a very small capacity already leads to a cosiderably high bitrate (due to the cotributios of a large populatio of idle users), hece the icrease of bitrate is more sigificat uder a small capacity (as the bechmark bitrate is smaller). With 6% video users, the icrease of bitrate first icreases with the lik capacity (due to a similar reaso i the % case), ad the decreases with the lik capacity (due to a similar reaso i the % case). The maximum bitrate icrease ratio uder the full cooperatio sceario ca be up to 5% 3% with % video users, 35% 6% with 6% video users, ad 4% 4% with % video users. Moreover, the bitrate icrease uder the real data traces is bouded by the above maximum ratio, ad actually depeds o the ecouterig probability. I our experimets, the bitrate icreases uder ISF ad NWF ca reach aroud 6% ad 4% of the maximum bitrate icrease, respectively. ) Social Welfare: Figure 5 shows the average social welfares ad welfare gais with differet percetages of video users uder differet etwork coditios. The key iformatios ad observatios regardig the social welfare are similar as those regardig the average bitrate i Figure 4, hece we skip the detailed discussios ad oly preset the results regardig the cooperatio gai. Specifically, usig our proposed algorithm, the maximum social welfare icrease ratio (uder the full cooperatio sceario) ca be up to % 4% with % video users, % % with 6% video users, ad 5% 5% with % video users. The social welfare icrease uder ISF ad NWF ca reach 6% ad 4% of the maximum welfare icrease, respectively. 3) Algorithm Compariso: From Figure 4 (a) to (c) ad Figure 5 (a) to (c), we ca also evaluate the performace differece betwee our proposed algorithm ad the algorithms i [6] ad [7] i the multi-user sceario. By comparig the differece betwee solid bars (for the o-cooperative sceario) ad the differece betwee hollow bars (for the multi-user cooperative sceario), we ca fid that the performace differece (betwee our algorithm ad the algorithms i [6], [7]) become more sigificat i the cooperative sceario, especially whe the video user percetage is small. Such a performace differece is maily due to the o-optimal segmet ower selectio i [6], [7]. I our algorithm, however, the segmet ower selectio ad the bitrate adaptatio are optimised joitly. 6% %

13 Average Social Welfare.5.5 Multi-User Case (% Video Users) Full Cooperatio (A) ISF (B) NWF No Cooperatio ~.7 ~.3 ~.5 ~5. ~8. Average Lik Capacity (Mbps) Average Social Welfare.5.5 Multi-User Case (6% Video Users) ~.7 ~.3 ~.5 ~5. ~8. Average Lik Capacity (Mbps) Average Social Welfare.5.5 Multi-User Case (% Video Users) ~.7 ~.3 ~.5 ~5. ~8. Average Lik Capacity (Mbps) Average Social Welfare Icrease % Video Users 6% Multi-User Case Lyapuov-Based Algorithm % ~.7 ~.3 ~.5 ~5. ~8. Average Lik Capacity (Mbps) (a) (b) (c) (d) Figure 5. (a) Social Welfare with % Video Users, (b) Social Welfare with 6% Video Users, (c) Social Welfare with % Video Users, (d) Average Social Welfare Icrease uder the Lyapuov-based Algorithm. 8 CONCLUSION I this work, we proposed a multi-user cooperative video streamig framework for video streamig over wireless etworks. We aalyzed the theoretical performace boud of the proposed cooperative streamig system, ad desiged the olie streamig algorithm for the practical implemetatio. We coducted extesive experimets with real data traces, ad illustrated both the cooperatio gai of the cooperative streamig system ad the performace gai of the proposed olie streamig algorithm. Adaptive bitrate streamig is a ew techology tred of mobile video streamig, ad the research o multi-user cooperative video streamig is becomig icreasigly importat. This paper developed a uified cooperative framework, for both theoretical aalysis ad practical implemetatio. There are several iterestig future research directios i this area. A importat oe is to cosider the users strategic behaviours ad the associated icetive issues i the cooperative streamig. 9 ACKNOWLEDGMENTS This work is supported by the Natioal Natural Sciece Foudatio of Chia (Grat No. 6776, 6474, ad 65) ad the Geeral Research Fuds (Project Number CUHK 4635 ad CUHK 496) established uder the Uiversity Grat Committee of the Hog Kog Special Admiistrative Regio, Chia. This work is also supported by the Beijig Key Lab of Networked Multimedia (Z6565). Jiawei Huag is the correspodig author. REFERENCES [] Cisco VNI: Global Mobile Data Traffic Forecast Update, 6-. [] S. Akhshabi, Ali C. Bege, ad C. Dovroli, A Experimetal Evaluatio of Rate-Adaptatio Algorithms i Adaptive Streamig over HTTP, Proc. ACM MMSys,. [3] Adobe Systems, HTTP Dyamic Streamig, url: adobe.com/products/hds-dyamic-streamig.html [4] R.P. Patos, HTTP Live Streamig draft-patos-http-livestreamig-3, Network Workig Group, 4, url: ietf.org/html/draft-patos-http-live-streamig-3 [5] Microsoft, Smooth Streamig, url: dowloads/microsoft/smooth-streamig [6] T. Y. Huag, R. Johari, N. McKeow, M. Truell, ad M. Watso, A buffer-based approach to rate adaptatio: Evidece from a large video streamig service, Proc. ACM SIGCOMM, 4. [7] Z. Li, X. Zhu, J. Gahm, R. Pa, H. Hu, A. C. Bege, D. Ora, Probe ad adapt: Rate adaptatio for http video streamig at scale, IEEE Joural o Selected Areas i Commuicatios, 3(4):79-733, 4. [8] T. V. Seeivasa, M. Claypool, CStream: eighborhood badwidth aggregatio for better video streamig, Multimedia Tools ad Applicatios, 4. [9] Y. Zhag, C. Li, L. Su, DECOMOD: collaborative DASH with dowload ehacig based o multiple mobile devices cooperatio, Proc. ACM MMSys, 4. [] N. Golrezaei, P. Masourifard, A. F. Molisch, ad A. G. Dimakis, Base-Statio Assisted Device-to-Device Commuicatios for High-Throughput Wireless Video Networks, IEEE Trasactios o Wireless Commuicatios, 3(7): , 4. [] M. Ramada, L. EI Zei, ad Z. Dawy, Implemetatio ad evaluatio of cooperative video streamig for mobile devices, Proc. IEEE PIMRC, 8. [] L. Keller, A. Le, B. Cici, H. Seferoglu, C. Fragouli, ad A. Markopoulou MicroCast: Cooperative Video Streamig o Smartphoes, Proc. ACM MobiSys,. [3] Y. Cao, X. Che, T. Jiag, ad J. Zhag, SoCast: social ties based cooperative video multicast, Proc. IEEE INFOCOM, 4. [4] W. Pu, Z. Zou, ad C. W. Che, Video adaptatio proxy for wireless dyamic adaptive streamig over HTTP, IEEE Workshop Packet Video,. [5] P. Georgopoulos, Y. Elkhatib, M. Broadbet, M. Mu, ad N. Race, Towards etwork-wide QoE fairess usig opeflow-assisted adaptive video streamig, ACM Workshop o Future Huma- Cetric Multimedia Networkig, 3. [6] M. Tag, S. Wag, L. Gao, J. Huag, ad L. Su, MOMD: A Multi-Object Multi-Dimesioal Auctio for Crowdsourced Mobile Video Streamig, Proc. IEEE INFOCOM, 7. [7] X. Kag ad Y. Wu, Icetive Mechaism Desig for Heterogeeous Peer-to-Peer Networks: A Stackelberg Game Approach, IEEE, 4(5):8-3, 5. [8] M. Klusch, P. Kapahke, X. Cao, B. Raier, C. Timmerer, ad S. Magold, MyMedia: mobile sematic peer-to-peer video search ad live streamig, Proc. ACM MOBIQUITOUS, 4. [9] B. Raier, C. Timmerer, P. Kapahke, ad M. Klusch, Real-time multimedia streamig i ustructured peer-to-peer etworks, Proc. IEEE CCNC, 4. [] G. Iosifidis, L. Gao, J. Huag, ad L. Tassiulas, Icetive Mechaisms for User-Provided Networks, IEEE Commuicatios Magazie, 5(9):-7, 4. [] G. Iosifidis, L. Gao, J. Huag, ad L. Tassiulas, Eablig Crowd- Sourced Mobile Iteret Access, Proc. IEEE INFOCOM, 4. [] Ope Garde, url: [3] Karma, url: 3

14 4 [4] Tetherig of AT&T, url: [5] K. Mori, S. Hatakeyama, H. Shigeo, DCLA: Distributed Chuk Loss Avoidace Method for Cooperative Mobile Live Streamig, Proc. IEEE AINA, 5. [6] T. Wu, W. Dou, Q. Ni, S. Yu, ad G. Che, Mobile Live Video Streamig Optimizatio via Crowdsourcig Brokerage, IEEE Trasactios o Multimedia, 9():67-8, 7. [7] IgKee, url: [8] Youtube Live, url: dashboard splash [9] Facebook Livestream, url: [3] T. Luo, S. S. Kahere, J. Huag, S. K. Das, ad F. Wu, Sustaiable Icetives for Mobile Crowdsesig: Auctios, Lotteries, ad Trust ad Reputatio Systems, IEEE Commuicatios Magazie, 55(3):68-74, 7. [3] M. Tag, L. Gao, H. Pag, J. Huag, ad L. Su, Optimizatios ad Ecoomics of Crowdsourced Mobile Streamig, IEEE Commuicatios Magazie, 55(4):-7, 7. [3] T. Luo, S. K. Das, H. P. Ta, ad L. Xia, Icetive Mechaism Desig for Crowdsourcig: A All-Pay Auctio Approach, ACM Trasactios o Itelliget Systems ad Techology, 7(3):35:-6, 6. [33] D. Yag, G. Xue, X. Fag, ad J. Tag, Icetive mechaisms for crowdsesig: Crowdsourcig with smartphoes, IEEE/ACM Trasactios o Networkig, 4(3):73-744, 6. [34] D. Yag, G. Xue, X. Fag, ad J. Tag, Crowdsourcig to smartphoes: Icetive mechaism desig for mobile phoe sesig, Proc. ACM Mobicom,. [35] X. Zhag, Z. Yag, W. Su, Y. Liu, S. Tag, K. Xig, ad X. Mao, Icetives for mobile crowd sesig: A survey, IEEE Commuicatios Surveys & Tutorials, 8():54-67, 6. [36] F. Restuccia, S. K. Das, ad J. Payto, Icetive mechaisms for participatory sesig: Survey ad research challeges, ACM Trasactios o Sesor Networks, ():3:-4, 6. [37] L. G. Jaimes, I. J. Vergara-Laures, ad A. Raij, A survey of icetive techiques for mobile crowd sesig, IEEE Iteret of Thigs Joural, (5):37-38, 5. [38] F. Restuccia ad S. K. Das, Fides: A trust-based framework for secure user icetivizatio i participatory sesig, Proc. IEEE WoWMoM, 4. [39] J. Nash, The Bargaiig Problem, Ecoometrica, 95. [4] T. Camp, J. Boleg, ad V. Davies, A survey of mobility models for ad hoc etwork research, Wireless Commuicatios ad Mobile Computig, (5):483-5,. [4] P. Yua ad H.-D. Ma, Opportuistic Forwardig with Hotspot Etropy, Proc. IEEE WoWMoM, 3. [4] N. Balasubramaia, A. Balasubramaia, ad A. Vekataramai, Eergy cosumptio i mobile phoes: a measuremet study ad implicatios for etwork applicatios, Proc. ACM SIGCOMM, 9. [43] M. A. Hoque, M. Siekkie, ad J. K. Nurmie, Eergy efficiet multimedia streamig to mobile devicesła survey, IEEE Commuicatios Surveys & Tutorials, 6(): , 4. [44] G. P. Perrucci, F. H. Fitzek, ad J. Widmer, Survey o eergy cosumptio etities o the smartphoe platform, Proc. IEEE VTC-Sprig,. [45] M. J. Neely, Stochastic Network Optimizatio with Applicatio to Commuicatio ad Queueig Systems, Morga & Claypool,. [46] [47] [48] [49] Olie Techical Report, url: ph4rh5codd3lu/appedixeffstreamig.pdf?dl= Li Gao (S 8-M -SM 6) is a Associate Professor with the School of Electroic ad Iformatio Egieerig, Harbi Istitute of Techology, Shezhe, Chia. He received the Ph.D. degree i Electroic Egieerig from Shaghai Jiao Tog Uiversity i. His mai research iterests are i the area of etwork ecoomics ad games, with applicatios i wireless commuicatios ad etworkig. He received the IEEE ComSoc Asia-Pacific Outstadig Youg Researcher Award i 6. Mig Tag (S 6) is curretly pursuig a Ph.D. degree at the Departmet of Iformatio Egieerig, The Chiese Uiversity of Hog Kog (CUHK). Her research iterests iclude wireless commuicatios ad etwork ecoomics, with particular emphasis o user-provided etworks, mobile video streamig, ad fog computig. Haitia Pag (S 6) received his BE degree i Departmet of Automatio i 4 from Tsighua Uiversity, Beijig, Chia. He is curretly a PhD cadidate i Computer Sciece i Tsighua Uiversity. His research areas iclude etwork game modelig, cellular-wifi etworkig, video streamig system desig, ad mobile etworkig optimizatios. Jiawei Huag (F 6) is a Professor i the Departmet of Iformatio Egieerig at The Chiese Uiversity of Hog Kog. He is the co-author of 9 Best Paper Awards, icludig IEEE Marcoi Prize Paper Award i Wireless Commuicatios. He has co-authored six books, icludig the textbook o Wireless Network Pricig. He has served as the Chair of IEEE TCCN ad MMTC. He is a IEEE ComSoc Distiguished Lecturer ad a Thomso Reuters Highly Cited Researcher. Lifeg Su was bor i 97. He received the Ph.D. degree i System Egieer from the Natioal Uiversity of Defese Techology, Chagsha, i. Curretly, he is a professor at Tsighua Uiversity. His research iterests iclude video streamig, video codig, video aalysis ad multimedia cloud computig. He is a member of IEEE ad ACM. He received Best Paper Award i IEEE Trasactios o Circuits ad Systems for Video Techology i, Best Paper Award at ACM Multimedia, ad Best Studet Paper Award at MMM 5 ad IEEE BigMM 7.