User-level Fairness Delivered: Network Resource Allocation for Adaptive Video Streaming

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1 User-level Fairness Delivered: Network Resoure Alloation for Adaptive Video Streaming Mu Mu, Steven Simpson, Arsham Farshad, Qiang Ni, Niholas Rae Shool of Computing and Communiations, Lanaster University LA 4WA Lanaster, United Kingdom Abstrat HTTP adaptive streaming (HAS) tehnology is beoming a popular vehile for online video delivery. HAS appliations often ompete for network resoures without any oordination between eah other in a shared network. This leads to quality of experiene (QoE) flutuations and unfairness between end users. This paper introdues a user-level fairness model () whih exploits video quality, swithing impat and ost effiieny as the fairness metris to ahieve user-level fairness in resoure alloation. Experimental results demonstrate how this model is a foundation to orhestrate the resoure onsumption of HAS streams. I. INTRODUCTION Globally, IP video traffi is estimated to be 79% of all onsumer Internet traffi in 28, up from 66% in 23 [3]. HTTP adaptive streaming (HAS) protools, espeially the MPEG Dynami Adaptive Streaming over HTTP (DASH), are beoming popular for online video delivery, thanks to their unique adaptation feature that allows dynami seletion of quality representations in the fae of network flutuation. Most HAS protools adopt TCP as the transport layer protool whose retransmission mehanism greatly mitigates the impat of network impairments, suh as paket loss, to the delivered video quality. TCP aims to inrease bandwidth utilization whilst avoiding ongestion, whih allows the appliation to fully exploit the available network resoures. The adaptation between representations is often managed at the lient side and is not speified by the DASH standard. Reent years have seen an inreasing number of single-stream HAS optimization algorithms [], [22], [4], [9] with the main objetive being to maximize the quality of user experiene of a single HAS stream by exploiting bandwidth estimation, lient-side buffer management, and QoE measurement. However suh optimization algorithms work on user lients independently without any oordination with other devies in the same network. This leads to QoE flutuations and unfairness when multiple HAS streams on heterogeneous user devies ompete for network resoures. The onventional fairness models suh as proportional fairness [2] are not suitable for HAS appliations where video streams of different harateristis exhibit distintive utilities. Additionally, ruial HAS QoE impat fators suh as the representation swithing impat are often negleted. This paper presents a user-level fairness model to be exploited by either of the orhestration frameworks. The /5/$3. 25 IEEE model inorporates video quality, swithing impat and ost effiieny as the fairness metris, and provides reommendations on resoure alloation with respet to the partiular balane to be ahieved between the three fairness metris. We arried out a number of experiments to verify the performane of the model against the onventional network provisioning mehanisms with different degrees of network flutuations and various numbers of ompeting user lients. We also investigated how eah dimension of fairness ontributes to the overall user-level fairness as well as the impat of the model to the user pereived video quality for HAS streams. The results demonstrate the benefits of enabling user-level fairness as a servie for future networks. The remainder of the paper is organized as follows. Setion II explores bakground and related work in the field of network management for HAS streams. The design of the model is detailed in Setion III. Setion IV introdues the experiment set-up as well as the results. Disussions and onlusions are given in Setion V and VI respetively. II. BACKGROUND AND RELATED WORK Using MPEG-DASH as an example, HAS ontent is prepared in multiple representations, eah of whih is a version of the same ontent prepared using different enoding speifiations (suh as bitrate and resolution) aording to predefined use senarios. A number of assoiated representations formulate an Adaptation Set. Eah representation is served with time-oded hunks addressable using URLs and retrievable via HTTP. By exploiting the swithing points between hunks, seamless swithing between representations beomes possible. Upon a playbak request, the user lient reeives a Media Presentation Desription (MPD), the manifest file that speifies all the resoures and struture information required for video retrieval and playbak. A DASH lient often starts from a representation that mathes its sreen resolution and at modest bitrates. One the lient detets an inrease in available bandwidth, it an swith to a higher bitrate. Most HAS protools adopt TCP as the transport mehanism. TCP aims to inrease bandwidth utilization whilst avoiding ongestion so that HAS appliations an fully exploit the network resoures and potentially maximize the quality of the delivered ontent. However, this presents two hallenges in delivering a good quality of user experiene espeially in a shared network environment with heterogeneous user devies. The first hallenge is the network flutuation aused by the

2 paket delivery sheme. A DASH lient may ontinuously inflate its reeiver window during TCP ON periods. This inadvertently fores the sender to burst as muh traffi as possible on to the network, until either enough video hunks are buffered at the lient (whih then swithes to OFF mode), or until the sender inurs TCP paket loss. This behavior auses extremely bursty traffi and TCP ineffiieny, as onnetions are repeatedly restarted between ON and OFF periods, resulting in unstable video playbak quality[], [9]. The seond hallenge is the gap between network-level fairness and userlevel fairness when network resoures are shared between multiple HAS streams on different devies over heterogeneous networks. Conventionally, the goal of resoure alloation in the network is to maximize the aggregate utility of all the users in the network subjet to the apaity onstraints of the network [2], [7]. One known implementation based on this theory is proportional fairness [2]. Proportional fairness performs well when all users follow the same utility. When users have different QoS needs, proportional fairness favors users who require lower rates to ahieve high utility. Therefore, maximizing the ombined utility does not neessary lead to fairness between users. In fat, researh work in the field of video quality analysis shows that there is no linear orrelation (video quality utility) between the bitrate of a video stream and its pereptual quality [2]. This suggests that an inrease in bitrate ould lead to a signifiant gain in resulting quality or very limited quality improvement that is barely notieable to the end user. In order to optimize the effiieny of network resoure alloation whilst maintaining a satisfatory level of user experiene, it is essential to inorporate the video-quality utility of media streams. Reent years have seen signifiant work put forward to improve the QoE of HAS (partiularly MPEG-DASH) video distribution. One solution is to have some ross-layer interation between TCP and HTTP in order to provide the streaming appliation with better metris and to allow TCP to reah steady-state [9]. This would indeed improve TCP performane, but would not ontrol the ON/OFF nature of DASH-style appliations. Furthermore, it would not attain network-wide fairness aross all devies. Tian and Liu [22] use throughput-predition algorithms to attenuate video rate flutuations. Mansy et al. [8] have shown that DASH s bursty nature leads to exessive queuing in the network (a phenomenon ommonly referred to as bufferbloat [7]), and they proposed adjusting DASH s buffering behavior to keep the size of the lient s reeiver window low. FESTIVE [] attempts to improve fairness, stability and effiieny using a DASH player with a stateful, delayed-bitrate update mehanism. Huang et al. introdue a buffer-based approah to rate adaptation to redue the rebuffer rate in online HAS streaming []. A lientside rate-adaptation algorithm for HAS is introdued in [4]. Georgopoulos et al. inorporated OpenFlow and video quality utility as part of work towards network-wide QoE fairness [6]. Most of the aforementioned work fouses on optimizing the network effiieny or the quality of user experiene of individual media streams. There is urrently a lak of researh addressing the user-level fairness of network resoure provisioning in a multi-has-stream environment. With the inreasing number of high-throughput HAS streams delivered in IP networks, quality assurane via over-provisioning beomes less feasible. It is ruial for the servie providers to depart from simply providing best-effort networks to orhestrating the network resoure onsumption with a better understanding of the user-level requirements of user appliations, espeially the resoure-intensive HAS appliations. The utility and QoE metris are defined to a lass of appliation suh as HAS streaming and not ustomized for a servie provider. Hene, our proposition also does not onflit with the framework of network neutrality. III. USER-LEVEL FAIRNESS MODEL The ultimate goal of the user-level fairness model is to orhestrate network resoure alloation between HAS streams to improve the QoE fairness. The model takes into aount multiple fairness metris suh as video quality (fidelity of video frames), HAS adaptation impat as pereived by the end user, as well as the effiieny of media distribution in ahieving user experiene from the perspetive of servie providers. The underlying priniple of the model is depited in Figure. Fig.. User-level fairness model The model inorporates three fairness metris: videoquality fairness, swithing-impat fairness and ost-effiieny fairness. The metris address either the fairness between HAS streams using QoE measurements, or the fairness between network providers and resoure onsumers as a whole. The fairness metris are evaluated using: ) input parameters inluding ontext information regarding HAS streams suh as urrent playbak bitrate, resolution, et., 2) urrent network apaity of user devies, and 3) total bandwidth to share between multiple devies. Both the network apaity and the total bandwidth are dynami and an be affeted by the hange of link apaity or bakground traffi. The input parameters

3 an be derived using a network-level or appliation-level QoE framework. The atual extration of input parameters is further disussed in our related work [4]. One the model determines the best resoure alloation strategy to warrant userlevel fairness based on given information, reommendations an be forwarded to the network management funtions for QoE-aware network management suh as metering or QoE routing. Reent advanes in networking, suh as softwaredefined networking (SDN) and network-funtion virtualization (NFV), enable network-wide flexibility and programmability allowing omprehensive network and servie funtions to be deployed easily in an on-demand fashion. A. Video quality fairness In order to fairly share network resoures between HAS streams with respet to the QoE, it is ruial to understand and model the impat of network impairments on the delivered video quality. A HAS appliation hooses an optimal resolution that best mathes the native resolution of the playbak devie and dynamially selets a representation (of a ertain bitrate) from the adaptation set of the same frame resolution aording to available bandwidth. Based on the assumption that the same enoding sheme (e.g., Group of Piture struture, motion estimation shema, et.) is employed, a higher enoding bit-rate results in less ompression loss and therefore yields higher video quality pertaining to piture fidelity. A video-quality utility (VQ) funtion (a type of rate-distortion funtion) is often employed to apture the relationship between bitrate and video quality. It aptures the notion of the law of diminishing returns [2] a ertain addition of resoures to what one already has inreased the total worth, but it ontributes less and less to the inrease if one has more of the resoure already. We adopt the utility funtions derived in our previous work [6] as the foundation of the QoE modelling. Figure 2 shows the satter plot and the fitted utility urves of the HAS video bitrate utility in the three named resolutions. The figure reflets the ommon understanding that more resoures are required to deliver video of the same quality on higher resolutions. The utility plot quantifies suh a relationship between video quality and bitrate. Fig. 2. Video Quality p 72p 8p Bitrate (kbps) Satter plot and utility urves of bitrate utilities Q res = ar b + () Q 72p = 4.85r (2) Equation is the generi QoE utility funtion. r denotes the video bitrate and the Q is the video quality. A Q ofis the maximum possible video quality (when no ompression or lossless ompression is applied to the ontent). A utility funtion U res (r) is often developed to apture the relative video fidelity (Q res (r)) ompared with Q MAX res. With the Q MAX res resaled to be as the referene, Q res (r) has the data range of (,]. a, b, and are the oeffiients that instantiate the utility funtion for ertain video resolutions. For instane, Equation 2 is an instane of utility funtion for 72p videos. The utility funtion is of low omplexity (suitable for real-time quality assessment) and yet offers high performane. Equation 2 shows signifiant orrelation (R 2 of.9983 and RMSE of.2923) to the observed experimental results in [6]. The standard utility funtion U res (r) provides quality estimation based on the theoretially highest quality. In pratie, ontent providers offer a finite set of representations in an adaptation set and the highest quality offered for the ontent (Q TOP res ) is defined in a ontent manifest suh as the MPD by the best representation (r = r TOP ) in the adaptation set. By adopting suh a ompanion ontent desriptor provided by the ontent provider, we prevent the over-provisioning of resoures beyond the apaity of HAS streams and therefore avoid HAS streams with higher top representations being penalized. To this end, we are able to further improve the video quality fairness measurement by inorporating both the modeling of user pereption and the nature of media appliations. We resale U res (r) so that the video quality reahes its maximum value when the best representation is ative (i.e., Q TOP res = U res(r TOP )=). Hene the adjusted video quality utility funtion is: U res(r) = U res(r) U res (r TOP (3) ) In pratie, the maximum feasible quality of a stream is also limited by the network apaity at the user devie. The network apaity then determines the highest bitrate feasible (r MAX ) for the orresponding media stream. For instane, a user may have only 2 Mbit/s network apaity using Wifi networks in the garden, though she subsribes to 5 Mbit/s broadband network over DSL. Hene, it is not neessary to provision more than 2 Mbit/s of network resoure on the shared aess network for this user. Network apaity is often determined by the link apaity and any bakground traffi on the same link. The video quality utility is therefore tuned to reflet the network resoure onstraint at a user devie: U res(r) = U res(r) U res (r MAX ),ifrmax < r TOP (4) Using suh video quality utility funtions, we an then divide network resoures between HAS media streams in a way that minimizes any disrepany between the delivered video quality on all HAS streams, hene ahieving the video quality fairness. Q = Q 2 =... = Q N (5)

4 U res (r )=U res 2 (r 2 )=... = U res N (r N ), (6) with r + r r N = B Overall, the optimal video quality fairness of a HAS media stream an be ahieved mathematially using Equation 6 as influened by the available bandwidth and the adaptation set given by the ontent provider. The fairness between media streams an be measured using Relative Standard Deviation (RSD) (Equation 8), obtained by multiplying the standard deviation s by and dividing this produt by the mean Q. RSD aptures not only the deviation but also the sale of the video quality differene. A small RSD means less differene between video quality pereived over related HAS streams, whih also suggests better fairness at a user level. s VQ = M (Q j M Q) 2 (7) j= I VQ = s VQ RSD = s VQ Q B. Swithing impat fairness HAS media streams have the apability of swithing between representations as the means to adapt to the available network resoure. The purpose of swithing an be inreasing the bitrate to improve video quality or reduing the bitrate to avoid buffer underrun and playbak stalling. However, the swithing proess itself may ause disturbane to the end user. The impat of quality swithes (SI) is influened by the amplitude and the distribution of swithing events [5]. The amplitude is determined by the pereption of video quality hanges between representations. We define suh quality hange as Δ VQ = Q Q with Q as the projeted video quality after the representation swith. A higher hange of video quality leads to more severe pereptual impat at the time of swith. In a related work, Liu et al. observed that the impat aused by inreasing swith is muh smaller than dereasing swith of the same sale [5]. The modeling of this advaned feature requires further subjetive experiments, whih will be arried out in our future work. A ruial aspet when modeling the HAS swithing impat is the forgiveness effet, whih aptures the psyhologial observations that, when followed by intat ontent, the impat of quality distortion degrades over time. The forgiveness effet related to video quality degradation was first studied and modeled by Seferidis et al. [2] and Hands [8]. One of the key findings from the user ratings is that the impat of quality distortion is redued to nearly 7% after 2 seonds. We inorporate the forgiveness effet (Equation 9) in our model based on the generalized model introdued by Liu et al [6]. Equation 9 is a funtion of intensity of quality hanges (Δ VQ ) and the duration of time sine a swithing event (t t i ). SI i (t) =(Δ VQ )e.5(t ti), t i is the time of the quality swith i (8) (9) Using Equation 9, the initial swithing impat will eventually diminish to a negligible value when t t i is suffiiently large. We onsider the QoE as the overall aeptability of a video session as pereived by human user. Therefore, we define % of initial swith impat as a residual influene that lasts for the user s entire viewing session. This means that the residual impat from multiple swithing events will aumulate till the end of the viewing session. The impat funtion is updated as: SI i (t) =max((δ VQ )e.5(t ti),.δ VQ ) () t i is the time of the quality swith i Figure 3(a) shows the video quality (VQ) for playing the test video with options to swith between different video bitrates in 72p video resolution. The figure demonstrates the non-linear mapping between the video bit-rate and the video quality. For instane, a swith between two very high bitrates shows less impat on the video quality ompared with the same amount of bitrate hange between representations of lower bitrates. Suh QoE measurements are valuable for both single-stream quality optimization and QoE fairness between media streams. Swithing impat aounts for the frequeny and distribution of hanges over the playing time. As demonstrated in Figure 3(b), high swithing impat an be aused by high video quality variation or small, but temporally lose, hanges. Video Quality Fig. 3. Video Quality Video Bitrate Video Playbak Time (s) (a) Video quality Impat of HAS adaptation Playing Bitrate (Kbps) Swithing Impat.5 Swithing Impat 22 Video Bitrate Video Playbak Time (s) (b) Swithing impat Through swithing impat measurement, we an evaluate a resoure alloation solution or ompare different solutions using the following swithing impat fairness funtion based on RSD: s SI = M (SI j SI) M 2 () j= I SI = s SI RSD = s SI (2) During network flutuations representation swithing is inevitable on one or multiple HAS streams. Swithing impat fairness aptures the impat of swithes throughout the entire life-yle of a HAS stream, and balanes suh impat between related HAS streams. As an example, a relatively high RSD measure on swithing impat suggests that one or more HAS streams have experiened more frequent or severe quality adaptation between representations. Using the SI fairness metri, Playing Bitrate (Kbps)

5 the model would be able to mitigate suh imbalane and potentially protet the playbak bitrate of ertain HAS streams from further variations. C. Cost effiieny fairness Content onsumers, espeially those having invested in highdefinition TV and broadband internet onnetions, expet ondemand movies or live football mathes to be delivered at the highest possible quality. Distributing video ontent in high definition, high framerate, and high olor depth with the ompanion multi-hannel high quality audio traks is only possible with a high degree of guaranteed network bandwidth. Suh requirements plae great hallenges on network operators, partiularly during prime time when a large amount of onurrent video streams must be supported by shared network resoures. High throughput video streams an also overwhelm vulnerable segments of delivery networks and deteriorate paket delivery of other appliations. It is in network operators interests to assure a high degree of user satisfation on HAS video streams whilst moderating any exessive utilization of network resoures. We define ost effiieny CT as a metri to apture the notion of fairness between ontent onsumers and network operators. CT of a target network segment (usually a bottlenek) is quantified as the required (or onsumed) bandwidth per unit of total targeted (or delivered) video quality. A high CT denotes low ost effiieny as it requires more bandwidth to deliver a unit of video quality. Given the bitrate of seleted representations of related video streams and their adjusted utility funtions U, CT an be quantified using Equation 3. Unlike video quality fairness and swithing impat fairness, whih apture the level of deviation in a quality metri between media streams, the ost-effiieny fairness is evaluated based on all related HAS media streams as a whole over the measured network segment. I CT = N i r i N U res i (r i ) i (3) It is also possible to determine the most (theoretially) osteffetive bandwidth-provisioning solution(s) using Lagrange multipliers to find the minimal value(s) of Equation 3 subjet N to the onstraint r i B. However, a fairness model built i entirely based on CT would most likely favor bitrates towards the lower end of the hart due to the nature of utility urves (Figure 2). Therefore, CT fairness should be in priniple exploited in balane with at least a omplementary metri suh as video quality. D. Fairness-aware resoure alloation Using video-quality fairness, swithing-impat fairness, and ost-effiieny fairness as the user-level metris, a QoE servie an dynamially program speifi segments of a network using platforms like SDN so that network resoures an be provisioned fairly with respet to the user pereption of video ontent, and ost effiieny of the network, to deliver good user experiene. Inorporating the fairness metris in prodution networks either as a network servie or a QoE middleware poses a number of hallenges. Firstly, the adaptation sets of HAS streams omprise disrete and finite representations, hene the optimal solution to share available bandwidth between media streams annot be derived diretly from any ontinuous utility funtions. Ultimately a deision is made from the many ombinations of representations of eah media streams. For the ase that N HAS streams, eah with M representations to adapt to, are present for bandwidth sharing, N M ombinations are available per fairness metri. This leads to the seond hallenge of omputational omplexity. Taking HAS streams of representations per resolution as an example, the presene of 4 streams results in upto, potential solutions for bandwidth sharing using a single fairness metri. Every new stream joining the network would inrease the number of ombinations tenfold, and the model is to be queried to provide a new provisioning solution whenever there is a major hange (e.g., a new lient joining) in the network. When multiple QoE metris are inorporated, the omplexity of the fairness model will also inrease aordingly. For stateless metris suh as VQ and CT, whih are independent from past status (e.g., bitrate), it is possible to employ tehniques suh as dynami programming to improve runtime performane [3]. Stateful metris like SI require historial information related to quality swithes from the start of the video session, hene it is more diffiult to redue their runtime omplexity. In order to improve the feasibility of the model for live resoure alloation optimization, we approah this hallenge with an optimization method of three internal stages. At the first stage, the framework uses the ontinuous VQ utility funtions to derive the theoretially optimal sharing of bandwidth whih ensures that an idential degree of video quality is delivered on all HAS streams. The proess also maximize the utilization of total available bandwidth to share. This is done by solving the following equation of adjusted utility funtions: U res (r )=U res 2 (r 2 )=... = U res N (r N ), (4) with r + r r N = BW Beause VQ utility funtions are monotonially inreasing, Equation 4 gives at most one set of results: ˆR = [ˆr, ˆr 2,..., rˆ N ]. The seond stage takes the optimal solution given by the first stage as the starting point, and onduts a bi-diretional searh of nearest representations (defined in MPD) of every optimal bitrate in ˆR. The searh returns one or two playbak rates for eah ˆr:[[r,r l h ], [r2,r l 2 h ],..., [rn l,rh N ]]. [rl i,rh i ] are the bitrates of representations that best approximate the optimal rate of ˆr i, with ri l ˆr i and ri h ˆr i. In the ases that ˆr i is higher than the highest representations or lower than the lowest representations, only ri l or rh i will be available. The searhing stage serves as a sreening proess that greatly redues the omplexity of resoure alloation between N streams with M

6 levels of representations from M N (exhaustive searh) to a muh more manageable andidate list C of a maximum of 2 N items. The last stage of the optimization proess evaluates the andidate list C using three fairness metris: VQ, SI and CT, and identifies the andidate that ahieves the best balane of fairness between all three metris. The proess begins by alulating video quality fairness I VQ, swithing impat fairness I SI, and ost fairness I CT of all in andidate list C. We then ontinue with a pooling proess by ombining all three measurements and deriving an overall rating for eah. Beause the fairness metris are in different sales, we resale the fairness measurements using the maximum value of the same metri as the resaling fator. For instane: I VQ I VQ = max(i VQ C ) (5) The resaled fairness measurement I has the sale of [, ]. Beause a higher value in our fairness metris denote lower fairness, I = represents the worst solution from all andidates with respet to a given fairness. Any value between and quantifies the level of improvement a solution ahieves on a fairness metri ompared with the worst solution. Using the resaled fairness measurements, we then ombine the three fairness measurements using the weightedsum method: logs, whih apture time-oded representation hanges for all media streams, are also maintained for stateful metris suh as SI fairness. In order to study the harateristis of eah fairness metri in ahieving user-level fairness, we employed three additional fairness models, eah exlusively uses one of the three fairness metris (VQ, SI, and CT) to diret resoure alloation. We also inorporate a model, whih resembles how network resoures are provisioned through transport protools without the help of an overarhing orhestration framework. Together with the model, there are five fairness models (i.e., VQ, SI, CT,, and ) to be studied in the experiment. The testbed is therefore designed to arry out any experiment with five independent threads, eah hosting one of the five fairness models. As a result, we are able to omparatively study the results given by eah model based on idential test onditions. I ombined =w VQ I VQ with w VQ + w SI + w SI I SI + w CT + w CT = I CT (6) w is the weight oeffiient for eah fairness metri and it defines how fairness of video quality, swithing impat and ost is balaned. We define the model by reahing an equal balane between three fairness metris (i.e., w VQ = w SI = w CT ). This standard onfiguration help us investigate the impat of eah fairness metri to the overall resoure alloation solution. In pratie, a QoE management framework may adopt a different balane between video quality fairness, swithing impat fairness and ost fairness aording to speifi preferenes. The andidate solution whih exhibits the ombined minimum value of ombined fairness I is onsidered to be the best option to ahieve the overall user-level fairness. IV. EXPERIMENTS In order to assess the effetiveness of the model under different network onditions, we use a purpose-built evaluation testbed (Figure 4). Using profiles speified by a tester (e.g., number of lients, frequeny of network flutuations, lient link apaities, et.), the test sripter funtion generates randomized network events for test manifests. A testbed funtion parses any given test manifest and simulates lient arrival/departure and network flutuation aordingly. The resoure-alloation funtion enapsulates a number of APIs whih allow the testbed to speify network status and metadata of HAS streams, and aquire solutions to optimize resoure alloation between all relevant media streams. Session Fig. 4. Evaluation testbed Tests are speified using a generi tree struture. A number of lients (,..., N ) onnet to an aggregation node via orresponding links (l,...,l N ) and share the resoure of an aess network. One HAS stream is delivered to eah of the lients. The first test is defined with the duration of minutes and four lients (streams). The total available bandwidth aessible to video streams on the aess network randomly flutuates between 2Mb/s to 8 Mb/s as influened by bakground traffi. The resolution of video streams delivered to the lients is randomly seleted between 36p, 72p, or 8p. The bitrates for eah resolution are given in Table I. The available bandwidth on eah lient link also hanges randomly between 5kb/s to 8Mb/s during the ourse of the test to simulate hange of link apaity (suh as in wireless networks) or bakground traffi. The total available aess network bandwidth is shared between all video streams with respet to the resoure available on lient links. For the first test, we ompare how the model and fairness model provision network resoure differently in a dynami environment. Figure 5(a) and Figure 5(b) show how network resoure is shared between four video streams in the first 65 seonds of the test as instruted by the two different

7 TABLE I. SET BITRATES FOR THREE VIDEO RESOLUTIONS Resolution Video Bitrate (kbps) 8p, 2, 6,, 2, 4, 6, 8 72p, 2, 4, 6, 8,, 5, 2 36p, 2, 4, 6, 8, models. The resultant video quality of eah video stream is given in Figure 6(a) and Figure 6(b) for model and model respetively. The results learly demonstrate the signifiant differene of the network provisioning strategy adopted by the user-level model ompared with the onventional networklevel model. The model allows video streams with more intensive requests at the transport layer to aquire more resoures, leading to some video streams being heavily penalized (Figure 5(a)). Using the first 2 seonds of the test as an example, stream2, stream3 and stream4 all suffered from low video quality and severe quality flutuation while the quality of stream remains high through the entire test (Figure 6(a)). This example demonstrates the gap between networklevel and user-level fairness. Using the bespoke model, whih takes advantage of three fairness metris, the networkmanagement element in the testbed is able to shedule the resoure aording to the QoE requirements and link status of every HAS stream (Figure 5(b)). As a result, network resoures are dynamially provisioned in a way that similar video quality is maintained on all related media streams for the entire ourse of the experiment (Figure 6(b)). Furthermore, the model also avoided any severe video quality flutuation thanks to its inorporation of swithing-impat fairness. In order to further investigate the performane of the fairness model and speifially how eah individual fairness metri ontributes to the user-level fairness, we defined a test manifest similar to the first test and enabled all five fairness models. Test manifests are defined with respet to a test senario suh as busy wireless home network with a DSL broadband onnetion. It speifies the number of HAS streams, and the overall frequeny at whih the total shared bandwidth and the network apaity at user devies flutuate. The exat timing and sale of the dynamis are purposely randomized and will only be instantiated at run time. Therefore every iteration of the test will generate a unique test onfiguration of a predefined senario and hene test results. This also allows us to evaluate the onsisteny of the model. Exploiting suh a feature of the testbed, we repeated the test 5 times. Figure 7(a) ompares how five models perform in terms of video quality fairness. It reflets our previous observations in Figure 5 that the model signifiantly outperforms the model (a lower value in fairness metri denotes better fairness). Between the VQ, SI and CT models, VQ (whose objetive is to maximize the video-quality fairness exlusively without onsidering other fairness metris) yields the best results, unsurprisingly. The SI and CT models ompromise on video-quality fairness to balane swithing impat or ost fairness but still greatly outperform the model. The evaluation based on swithing impat fairness is shown in Figure 7(b). Similar to the onlusions in Figure 7(a), all user-level fairness models ahieve better performane than the Fig. 5. Video Playing Bitrate (bps) Video Playing Bitrate (bps) Stream Stream2 Stream3 Stream4 Total Available BW Video Playing Time(s) (a) Resoure alloation by model Stream Stream2 Stream3 Stream4 Total Available BW Video Playing Time(s) (b) Resoure alloation by model Resoure alloation of and model model, while we an maximize the swithing impat fairness using the SI model. The results delivered by the VQ and CT models are between SI and. The results on ost-effiieny fairness are slightly different (Figure 7()) to the other two. In this ase, the model exhibits better performane in delivering ost-effiieny fairness ompared with VQ, SI and, and is only beaten by the CT model. This is due to the fat that gaining a unit of video quality is easier when the bitrate of video is low aording to the video quality utility funtion whih resembles the law of diminishing returns. With more streams in the lower-bitrate and lowerquality ranges, the model an be more ost effetive in terms of onsumed bitrate per unit of delivered quality, though the delivered video quality is still muh lower than and other models as demonstrated in Figure 6(a). Overall, video-quality fairness, swithing-impat fairness and ost-effiieny fairness all exhibit their distintive benefits to the overall user-level fairness. A model ahieving the best on one fairness metri usually shows sub-optimal performane on the other two fairness metris. Experimental results suggest that by ombining the fairness models, it is possible to ahieve a good balane on all fairness measurements. In pratie, a shared network an be very quiet or extremely busy. To study the onsisteny of the model during network flutuations to various degrees, we speified new test manifests by manipulating the probability of a bandwidth hange using

8 Fig. 6. VQ Fairness Video Quality Video Quality Video Playing Time(s) Stream Stream2 Stream3 Stream4 (a) Video quality delivered by model Stream Stream2 Stream3 Stream Video Playing Time(s) (b) Video quality delivered by model Resultant video quality of and model VQ SI CT BASE Models (a) Video quality fairness measurement CT Fairness SI Fairness VQ SI CT BASE Models (b) Swithing impat fairness measurement VQ SI CT BASE Models () Cost effiieny fairness measurement the test sripter. We generated a total of 4 -minute-long tests with the number of bandwidth hanges varying from around 2 (one hange to the shared bandwidth every 3 seonds) to 2 (one hange to the shared bandwidth every 5 seonds). Every hange of the shared bandwidth leads to a realloation proess instruted by the resoure-alloation funtion. Again, we use both the and models for a omparative analysis. The inreasing number of quality flutuations is believed to have an impat on the model espeially through its stateful SI metri, where every hange of video quality is aounted for in the user experiene. Figure 8 ompares how the and models deliver user experiene on media streams using the three fairness measurements for networks of different harateristis. Eah point on the figure projets the mean value of the orresponding fairness measure of the entire test. The model ahieves its design objetives of delivering a good level of video quality fairness and swithing impat fairness whilst maintaining the ost effiieny ompared with the model. The SI fairness measurements are more sattered between tests of fewer bandwidth hanges than the tests of frequent bandwidth hanges (Figure 8(b)). This is due to the fat that swithing impat is a stateful metri that reognizes the dependeny between onseutive hanges of playbak bitrate. Hene, tests with larger numbers of quality swithes are more likely to statistially apture the performane of the a model on SI fairness. Furthermore, as defined in Equation 6, the model may be onfigured to balane between the three fairness metris equally (as for the experiment), or to favour ertain metri(s) with respet to a partiular servie strategy. For instane, a servie provider may allow a level of disrepany on video quality whilst giving more priority to maximizing the ost effiieny and minimizing the swithing impat. VQ Fairness Number of Clients (a) Video quality fairness measurement CT Fairness SI Fairness Number of Clients (b) Swithing impat fairness measurement Number of Clients () Cost effiieny fairness measurement Fig. 7. Fairness measurements of resoure alloation models Fig. 9. Performane of model influened by the number of lients One important performane index of a resoure alloation

9 VQ Fairness SI Fairness CT Fairness Number of Bandwidth Changes (a) Video quality fairness measurement Number of Bandwidth Changes (b) Swithing impat fairness measurement Number of Bandwidth Changes () Cost effiieny fairness measurement Fig. 8. Performane of model influened by the number of bandwidth flutuations algorithm is its salability. We ontinue the experiments using test manifests that allows different numbers of user lients (media streams), ranging from 4 (used by previous tests) to 4. The total bandwidth is onfigured to be flutuating between 2Mbps to 8Mbps for around 35 times during the ourse of the -minute-long test. This set-up helps us to investigate whether the model an perform with the same level of user-level fairness when more lients join the shared network and share the same pool of network resoures. Figure 9 suggests that inreasing the number of lients does not signifiantly impat the output of the model. There seems to be a trend of CT fairness redution when the number of lients inreases beyond. However, the fairness measurements still stay within the data range observed in Figure 8() where the number of lients is 4. V. DISCUSSIONS In Setion IV, we investigated the effetiveness of the model in ahieving user-level fairness using a tree-like network topology. The model, designed to be topology-agnosti, an be exploited for different use senarios where resoures are shared between multiple entities. For more omplex network strutures, the model may be applied reursively or in a level of abstration (e.g., resoure provisioning between two sub-networks). The goal of the model is to look beyond networklevel metris and maximize the fairness of resoure alloation at a user level. In other words, the model keeps end users equally happy by provisioning network resoures aording to appliation and user requirements. We onsider the model as our first step towards user-level fairness. There are still a number of hallenges to be addressed. For instane, the ultimate user-level fairness may ome at the ost of sarifiing the QoE of some HAS streams (suh as stream in Figure 6(a) and Figure 6(b)). It is worth studying whether ertain streams are overly penalized. In the worst ase, ahieving fairness may also result in all users being equally unhappy. Therefore, a balane has to be reahed between user experiene on individual HAS streams and the overall fairness between users. VI. CONCLUSIONS HAS is beoming a popular vehile for online video delivery. The adaptiveness of HAS maximizes the utilization of network resoures for better video quality and ensures smooth playbak during network flutuations. However, HAS appliations often work independently without oordination between eah other in the same network. This leads to QoE flutuations and unfairness between end users. This paper introdues a model, whih failitates the orhestration of network resoure alloation to ahieve user-level fairness. The model inorporates video quality, swithing impat, and ost effiieny as the fairness metris. The performane and salability of the model is evaluated through a number of experiments. Future work will look into employing the model and piloting in-network and transparent QoE management using tehnologies suh as software defined networking. ACKNOWLEDGEMENT The work presented is supported by the European Commission within FP7 projet FI-Content2 (grant 63662) and by the UK EPSRC projet TOUCAN (grant EP/L29/). REFERENCES [] S. Akhshabi, A. Begen, and C. Dovrolis. An Experimental Evaluation of Rate-adaptation Algorithms in Adaptive Streaming over HTTP. In Pro. 2nd annual ACM Conferene on Multimedia Systems, MMSys, pages 57 68, 2. [2] G. Cermak, M. Pinson, and S. Wolf. The relationship among video quality, sreen resolution, and bit rate. Broadasting, IEEE Transations on, 57(2): , 2. [3] Ciso. Ciso visual networking index: Foreast and methodology, paper pdf, 24. [4] A. Farshad, P. Georgopoulos, M. Broadbent, M. Mu, and N. Rae. Leveraging sdn to provide an in-network qoe measurement framework. In IEEE INFOCOM 25 Workshop on Communiation & Networking Tehniques for Contemporary Video. IEEE, 25. [5] M.-N. Garia, F. D. Simone, S. Tavakoli, N. Staelens, S. Egger, K. Brunnstrm, and A. Raake. Quality of experiene and http adaptive streaming: a review of subjetive studies. Proeedings of the 6th International Workshop on Quality of Multimedia Experiene (QoMEX), 24.

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