QoE-Oriented Resource Allocation for 360-degree Video Transmission over Heterogeneous Networks

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1 QoE-Orieted Resource Allocatio for 36-degree Video Trasmissio over Heterogeeous Networks Wei Huag, Liaghui Dig, Hug-Yu Wei Member, IEEE, Jeq-Neg Hwag Fellow, IEEE, Yilig Xu, ad Weju Zhag Fellow, IEEE, Cooperative Media Network Iovatio Ceter, Shaghai Jiao Tog Uiversity, Shaghai, Chia Departmet of Electrical Egieerig, Uiversity of Washigto, Seattle WA, USA Departmet of Electrical Egieerig, Natioal Taiwa Uiversity, Taipei, Taiwa arxiv:3.779v [cs.mm] Mar Abstract Immersive media streamig, especially virtual reality (VR)/36-degree video streamig which is very badwidth demadig, has become more ad more popular due to the rapid growth of the multimedia ad etworkig deploymets. To better explore the usage of resource ad achieve better quality of experiece (QoE) perceived by users, this paper develops a applicatio-layer scheme to joitly exploit the available badwidth from the LTE ad Wi-Fi etworks i 36-degree video streamig. This ewly proposed scheme ad the correspodig solutio algorithms utilize the saliecy of video, predictio of users view ad the status iformatio of users to obtai a optimal associatio of the users with differet Wi-Fi access poits (APs) for maximizig the system s utility. Besides, a ovel buffer strategy is proposed to mitigate the ifluece of shorttime predictio problem for trasmittig 36-degree videos i time-varyig etworks. The promisig performace ad low complexity of the proposed scheme ad algorithms are validated i simulatios with various 36-degree videos. Idex Terms Virtual reality (VR)/36-degree video; quality of experiece (QoE); field of view (FoV); saliecy; resource allocatio; multiple access techology; buffer maagemet. I. INTRODUCTION RECENTLY, immersive media has gaied icreasig popularity, icludig 36-degree/VR videos ad argumet reality/holograms, sice they ca provide people persoalized ad immersive experiece. Especially 36-degree/VR videos ca ow be easily perceived by users through head-mouted displays (HMDs). Most 36-degree/VR videos have a resolutio higher tha to provide a real immersive feel, therefore the desire for better immersio ad presece has placed ew demads o the etwork i terms of its quality ad performace, especially the quality of experiece (QoE) perceived by users. However, badwidth requiremets will become icreasigly imperative correspodigly for a highquality virtual reality experiece. Accordig to the data from [], VR traffic is poised to grow -fold by. Providers eed to take a ote of the ew demads ad ehace the experiece of users with limited badwidth. Tile-based videos have bee widely used i immersive media to eable the adaptive video trasmissio based o user s regio of iterests (ROIs). Typically, i 36-degree/VR videos, users ca oly see parts of the video at a certai time. If the server trasmits the whole video, most badwidth This paper has bee submitted to Digital Sigal Processig. will be wasted to trasmit the video ot visible to users. A tile-based method spatially partitios a 36-degree/VR video ito multiple segmets, which are called tiles. Some papers have proposed methods to trasmit the tiles accordig to the user s field of view (FoV) [] []. By predictig the users behavior ad gettig the FoV of users, oly tiles i the FoV are trasmitted with high quality while miimizig the quality of the rest of the video to save badwidth. However, the existig algorithms oly cosider a sigle user i trasmissio ad caot be easily exteded to wireless VR trasmissio with multiple users, sice the server eeds to cosider the rate allocatio o users ad o tiles simultaeously. Besides, they use a costat umber of tiles i the FoV, igorig the fact that the umber of tiles correspodig to the FoV will chage depedig o the viewpoit []. Moreover, users will pay more attetio o parts of the 36-degree videos correspodig to their FoV. These features i 36-degree video will drastically chage the trasmissio strategies. Researchers have ivestigated the saliecy i 36-degree videos [9] [], which is used to deote the most probable areas i a video a average perso will look at. Furthermore, it is poited out that average motio i a 36-degree video is less tha that for a regular video []. These characteristics ca be highly beeficial for reducig the badwidth cosumptio i 36-degree video trasmissio, oetheless, few papers ivestigate how to utilize these characteristics collectively with resource allocatio i wireless VR trasmissio. The evolutio of G techology has cotributed to providig massive improvemets for 36-degree videos i terms of badwidth ad reliability. A lot of researchers have tried to better exploit the badwidth by utilizig the heterogeeous LTE ad WLAN multi-radio etworks. They allow users access to such heterogeeous badwidth by operatig the multi-radio iterfaces simultaeously. Typical solutios iclude LTE-WLAN aggregatio techology [3] [] ad software-defied etworkig (SDN) used to help further exploit the heterogeeous resource flexibly [6], [7]. However, applyig these techologies to 36-degree videos requires solvig a series of problems, such as joitly optimizig the spatial ad temporal domais, cotrollig etwork associatio with multiple users, combiig the saliecy ad users field of view, ad achievig low complexity. Some researchers have ivestigated the 36-degree video streamig with these ew techologies [] []. Noetheless, their works are based

2 o sigle user ad detailed resource allocatio schemes are missig. The aforemetioed techologies ad methods rely o the prior iformatio of chael states. If the feedback fails to estimate the fluctuatio i time-varyig etworks, users may see a froze/blak scree ad wait for the arrival of the ext frame, resultig i a big drop of QoE. Playback buffer ca tackle this problem by storig videos i advace [] []. However, the scheme is ot suitable for FoV-drive 36- degree videos sice it is difficult to predict the FoV for a log time i the buffer, the server eeds to trasmit the whole video for those poorly predicted frames. Noetheless, a short buffer ca cause frequet re-bufferig evets ad result i the video pause ad low QoE whe the cliet s playback buffer goes empty []. O the other had, a log buffer ca icur poor predictio results ad also result i low QoE or wasted badwidth. I this paper, we propose a applicatio-layer (APP-layer) resource allocatio scheme for 36-degree/VR video trasmissios over multi-rat systems with multiple users. We joitly cosider how saliecy ad FoV positios ifluece the tilebased 36-degree videos i streamig. More specifically, we propose algorithms to decide which user should be coected to which Wi-Fi AP, ad choose appropriate trasmissio rates for each tile of each video, so that the overall system QoE (utility) ca be maximized. Additioally, to address the buffer problem of FoV-drive 36-degree video etworkig schemes, we propose a ovel buffer strategy to achieve a good tradeoff betwee the video quality ad the buffer legth. The strategy ca be combied with our proposed resource allocatio algorithms ad achieve better QoE for time-varyig etworks. I summary, our work makes cotributios as follows: ) We propose a ew 36-degree video trasmissio scheme, which combies saliecy ad FoV i wireless multi-rat etworks, to best improve overall QoE. ) To solve the mixed iteger NP-hard problem, we propose algorithms to fid promisig solutios. Particularly, we propose a ovel heuristic algorithm which ca solve the NP-hard problem effectively with very low complexity. 3) We joitly cosider the spatial ad temporal domais. By ivestigatig the impact of buffer legth o FoVdrive 36-degree video trasmissios, a ovel hierarchical buffer updatig strategy is proposed to esure a robust buffer size with relatively high utility. ) We show via simulatios with 36-degree videos that the proposed algorithms yield sigificat QoE improvemet over existig couterparts. The amout of performace ehacemet is more proouced whe the etwork is crowded. The rest of the paper is orgaized as follows: Sectio II itroduces tile-based 36-degree VR video ad the system framework. Sectio III presets the problem formulatio. Sectio IV tackles the problem ad proposes effective algorithms to solve it. Sectio V addresses the buffer maagemet problem i time-varyig etwork with ovel buffer strategy. Experimet results ad performace evaluatios are show i Sectio VI, followed by coclusios ad future works i TABLE I SYMBOLS AND NOTATIONS User idex N Total umber of users U The Utility of user A, B Normalizatio coefficiets of U j Tile idex J Total umber of tiles i oe video A, B Normalizatio coefficiets of U m Tile represetatio level idex M Total Number of tile represetatio D m Video rate of tile represetatio m θ The predictio filed of view o sphere ϕ Azimuth agle i spherical coordiate θ Polar agle i spherical coordiate ρ Guaratee probability of FoV predictio y Probable FoV idex Y Total umber of probable FoV for user P y () The probability of probable FoV for user D,j Video rate of tile j o user m Tile represetatio idex M Total umber of tile represetatio i server d Total trasmissio rate of user through LTE ad WLAN d LT E Trasmissio rate of user through LTE chael d wifi,i Trasmissio rate of user o AP i r LT E Achievable rate of user through LTE chael r wifi,i Achievable rate of user whe coected to AP i W,j Saliecy weight of tile j o user C,j m Cost of tile j o user whe m-th represetatio is selected Ũ,j m Improvemet utility of tile j o user whe m-th represetatio is selected ν,j m Utility over cost of tile j o user whe m-th represetatio is selected B Buffer threshold legth B Buffer maximum legth B c Curret buffer legth µ Coefficiet of QoE metric l Coefficiet of buffer strategy σ Coefficiet of pealty fuctio Sectio VII. Notatios: The symbols ad otatios used i this paper are summarized i Table I. II. PRELIMINARY AND SYSTEM MODEL A. Tile-Based 36-degree VR Video To make a tiled 36-degree VR video, oe ca resort to either multiple source camera setup or partitioig of a sigle video ito multiple frames of smaller resolutio. A example of a tile-based 36-degree VR video is show i Figure (a), where the video is cut ito tiles, By specifyig spatial relatioship descriptio i the file, the server ca assig differet etworkig resources to those tiles. I this paper, we joitly cosider the saliecy i the videos, FoV of users ad various chael status to allocate the resources. Saliecy ca be used i 36-degree videos to further improve the QoE [6], [7]. Compared with saliecy i traditioal videos, saliecy i 36-degree videos also takes ito accout the positio ad projectio [9] [], so that a saliecy map ca highlight the regios where most people will look at. A example of the saliecy map is show i Figure (b). O oe had, users have a higher chace to look at those parts with higher saliecy. O the other had, whe users lookig at the higher-saliecy portio of video, they will look more

3 3 (a) (b) Fig.. (a) A 36-degree video is partitioed ito titles. (b) The saliecy weights associated with each title. Whe it comes to 36-degree video, as we illustrated, the utility is oly meaigful i the FoV. Besides, the fial utility i the FoV is ot oly related to the sum of utilities of all tiles. More specifically, it has bee poited out whe the differece of rates betwee eighborig tiles is too large, the user ca be disturbed by the lowest rate i the FoV []. The overall QoE will be maily affected by that tile with bad quality eve though the rest of the tiles are of good qualities. Based o such defiitio, combig FoV ad saliecy weightig, we defie the video quality of a user as: j FoV U,j W,j + µmi j FoV (U(D,j )), () where µ is a tradeoff coefficiet, j is the idex of tiles i each video. D,j deotes the rate of tile j i user. The first part is the traditioal utility used i 36-degree video, which sums the idividual utilities of all tiles i the FoV. W,j is the saliecy weight for the tile j i user, which deotes the importace of visual perceptio the tile has. With the method i [9], [], we ca derive the saliecy score of each pixel i the D-scree for a 36-degree video. By givig ormalized score accordig to the saliecy results ad calculatig the sum of every pixel i each tile, we ca derive the saliecy weight of each tile as show i Figure (b). The secod part is used to make sure that the differeces of rates i user s view are ot too large. Thus, users will have a pleasat experiece as we show i the experimetal results. The tradeoff coefficiet µ should be cotet depedet ad is empirically determied i this paper. carefully o those parts, where distortios are easily perceived as more aoyig ad as such will receive a lower subjective quality score []. Therefore, we eed to allocate more rates o tiles with higher saliecy weights. However, a user ca oly view a part of the whole 36- degree video at a certai time, i.e., the FoV. A 36-degree video may have several high-saliecy weighted tiles which are ot i the FoV. Besides, some users may have their ow iterests to look at videos. Durig the trasmissio, those tiles out of the FoV will be meaigless to have higher rates. To best utilize the badwidth, we should cosider the saliecy i the FoV to improve the QoE. B. Utility Model We use the QoE as the performace metric i our paper, sice it has bee widely used for video etworkig quality evaluatio [9] [3]. It is kow that the user experiece is ot liearly proportioal to the video rate, sice it saturates at higher rates [3], [33]. Therefore, the utility accordig to the rate ca be defied as [3]. U(D) = A log B D D M, () where U deotes the utility of the video, D deotes video rate; video rate D {D,, D M } belogs to oe of the M predefied DASH-VR rate represetatios [3], [36] with D M beig the maximum rate the server ca provide. C. The system of Tile-based 36-degree Video Trasmissio i Multi-RAT Network Thaks to the easy deploymet of Wi-Fi APs, it is commo that users have access to several APs i the coverage of a LTE statio. To best explore the resource ad utilize Wi-Fi APs aroud users, we cosider a heterogeeous etwork to uicast multiple 36-degree videos to multi-users. The schemes ad algorithms we proposed ca also be exteded to multi-castig ad broadcastig scearios by utilizig groupig methods. Figure illustrates the detailed trasmissio of tile-based 36- degree VR videos. At the server side, the raw 36-degree videos will be divided ito tiles after projectios. The ecoder will geerate differet kids of represetatios for each tile. Saliecy detectio is also carried out at the server side to get the saliecy weight of each tile. All the tiles of 36- degree videos will be trasmitted through a heterogeeous LTE/WLAN multi-radio etwork. I this etwork, all users ca gai access to the LTE base-statio (BS) ad oe of the Wi-Fi APs simultaeously. This sceario ca be realized by LTE-WLAN aggregatio techology [3], [] or SDNbased LTE-WLAN multi-radio etworks [7]. I this paper, our proposed scheme ad algorithms are focused o APPlayer desig, the architecture ad the physical layer to support such cetralized heterogeeous system is out of the scope of this paper. At the cliet side, all tiles will be combied together after decodig. The rederig is used to help the 36-degree videos to preset to users. Buffer model is also

4 Server Side Ecodig tiles ito multiple qualities Projectios ad Saliecy Detectio Raw videos Make Represetatios Wi-Fi AP Cetralized Server Heterogeeous Network Cotroller LTE BS User Resource Allocatio ad Predictio Wi-Fi AP Feedback Chael Head/eye trackig Network iformatio Buffer iformatio Cliet Side Buffer ad display Rederig Decodig User User Wi-Fi AP 3 User Fig.. The system model of the proposed tile-based 36-degree video streamig over a multi-rat etwork. applied to cope with the problem whe the chael of etwork is varyig. Besides, a feedback lik is used to derive the achievable rate based o the chael state iformatio of each user [37], [3], as well as the behavior of users (such as headtrackig results) for helpig predict the FoVs. With all these iformatio, the cetralized heterogeeous etwork cotroller will help the video server to decide the rate allocatio for each tile ad the Wi-Fi AP associatio. III. RESOURCE ALLOCATION IN A MULTI-RAT NETWORK I the begiig of our trasmissio, users eed iitial frames to attract them to see the videos. Therefore, we allocate the rate o tiles based oly o the derived saliecy weights. With the trasmissio proceedig, users will have their ow iterests o the cotets. Predictio is used ad combied with the saliecy weights to further save the badwidth. To avoid the user viewig a blak scree whe the predictio algorithm is ot reliable o some users, the server will trasmit all the tiles at least i a very low represetatio. Our task is to decide the associatios of users to Wi-Fi APs as well as the rate allocatio for each tile through the heterogeeous etwork to maximize the total QoE. Thus, we formulate our FoV-drive trasmissio i a multi-rat system with multi-users i this sectio. A. FoV Probability I this paper, we segmet the 36-degree VR video ito tiles ad adopt equirectagular projectio (ERP), which is widely used i 36-degree video data represetatio. There are techiques developed to estimate the FoV for a shorter duratio ahead by aalyzig users viewig history ad head movemets (yaw ad pitch as show i Figure 3) [3], [39], []. However, the predictio may ot be accurate, thus, if we use predictio with low accuracy, the total QoE of users will have a high probability of beig extremely bad. Researches have also bee doe to give the predicted positio θ o sphere view with the correspodig predictio accuracy γ [3], [39]. Note that, throughout this paper, we assume that the FoV always cotais the whole tiles exactly ad if oly part of a tile is i the FoV, it will be treated as a whole tile. Therefore, there are always itegral umber of tiles i the FoV. Importatly, whe equirectagular projectio is used, the umber of tiles associated with the FoV is varyig depedig o the positio of FoV, e.g., the umber icreases as the viewpoit deviates from the equator. This ca be resolved by rotatig sphere geometry prior to projectio, however, which eeds to prepare much more represetatios with a optimal set of rotatio parameters. Therefore, without rotatio with respect to the FoV, we eed to map the predicted FoV o sphere (with φ ad θ to describe the positios) to the tiles o D scree accordig to the omidirectioal projectio relatioship provided by MPEG as show i Figure 3 []. Combiig the predictio agles, predictio accuracy ad the projectio relatioship, we ca derive the viewig probability of each probable FoV: P = P(θ, γ, φ, θ). Note that based o the predictio, oly several kids of FoV have ozero probability to be see by users. We sort them with the probability i a descedig order (idexed by y). They may cotai differet umber of tiles based o their positio, ad here we oly cosider Y kids of predicted FoV which satisfy Y y P ρ, where ρ is the guaratee probability. The, the expected QoE of a specific user is: Y U(D,j ) W,j + µ mi (U(D,j )) P y y (3) where P y deotes the viewig probability of F OV y i a D scree. U(D,j ) W,j + µ mi j FoVy (U(D,j ) is the correspodig QoE for F OV y as illustrated i Eq. (). We use the expected QoE as the objective fuctio of the optimizatio throughout this paper. B. Problem Formulatio Suppose there are oe LTE, I Wi-Fi APs (idexed by i), ad N users (idexed by ). Deote r LTE the maximum achievable rate of user based o its LTE chael quality, ad r,i wifi the maximum achievable rate user ca use whe coected to Wi-Fi i. Every 36-degree video is cut ito J

5 tiles (idexed by j). The video rate D,j of each tile i each user ca be chose from {D,..., D M }. W deotes the weight of each tile based o saliecy results. P y refers the viewig probability of probable FoV. The optimizatio variables are LTE trasmissio rates d LTE, =,..., N, Wi-Fi trasmissio rates d wifi,i, =,..., N, i =,..., I, the video rates D,j, =,..., N, j =,..., J for each tile of each user, ad users associatios o Wi-Fi AP. To maximize the expected QoE of all users accordig to our QoE metric defied i Eq. (3), the tile-based 36-degree video trasmissio problem formulatio ca be writte as follows: OPT- : N max s.t. j Y y U(D,j )W,j +µ mi U(D,j ) P y, D,j d,, () d = d LTE d LTE r LTE d wifi,i r wifi,i + i d wifi,i, (), (6), i, (7) card( [ d wifi,,, d wifi,i] ) =, () D,j {D,, D M }. (9) Eq. () implies that for a certai user, the sum of the video rates of each tile D,j should ot exceed the total trasmissio rate d allocated to it. Video rates of each tile ca oly be chose from the represetatios i the server as specified i Eq. (9). The trasmissio rate ca be aggregated from LTE ad WLAN etworks as illustrated i Sectio II.B. Eq. (6) models the competitio amog all users for the limited badwidth of LTE etwork: the sum of all trasmissio rates d LTE ormalized by r LTE is upper bouded by. The competitio for the Wi-Fi APs is specified by the costraits i Eq. (7). The costrait i Eq. () meas oly oe elemet of {d wifi,,, dwifi,i } is ozero, which eforces that each user ca oly be coected to a sigle Wi-Fi AP. the optimal solutio which also satisfies all the costraits. It is a mixed-iteger NP-hard problem which similar to the well-kow travelig salesma problem (TSP) []. I the followig, we cosider to relax the discrete search space of OPT-: D,j {D,, D m } to cotiuous search space D D,j D m, so as to make the problem computatioally tractable. Note that, after the relaxatio, if we fix the Wi-Fi AP coectio for each user, the problem becomes covex ad thus ca be solved by the covex optimizatio methods. A exhaustive search algorithm ca be used to test all the possible Wi-Fi associatios ad perform the rate optimizatio for each associatio. Although the exhaustive search ca guaratee to locate the optimal solutio of AP associatio, it has a complexity of O(I N ). A. Greedy Algorithm Algorithm Greedy Algorithm Variable defiitio: Q B : the set of fixed users ad correspodig associatio; Q C :the set of users have ot bee placed; (, i): the user is coected to Wi-Fi AP i; Iitial: Q B = Φ, Q C = {,,, N} ; : while Q C! = Φ do : sum = ; j = ; t = ; 3: for =, N, + + do : if Q C the : for i =, i I, i + + do 6: solve the OPT- whe user is fixed to Wi-Fi AP i; temp = max U QB +{(,i)}, D arg max U QB +{(,i)}; 7: if temp > sum the : sum = temp; j =, t = i; 9: ed if : ed for : ed if : ed for 3: update Q B Q B + {(j, t)}; Q C Q B {j} ad D ; : ed while : Roud D to appropriate represetatio. IV. RESOURCE ALLOCATION ALGORITHMS The method proposed i [] is effective to solve the 36- degree video trasmissio with a sigle user. However, it is ot applicable to such sceario with multi-user ad multi-rat take ito cosideratio, sice the badwidth/rate allocated to each user is ukow. Because the FoV ad the cotet viewed by users ca ifluece the rate allocatio, we caot cosider the rate for a user i a heterogeeous etwork ad the rate for each tile separately. A systematic algorithm eeds to decide them simultaeously. Besides, as see from Eq. (), the trasmissio rate d is joitly cotributed by the Wi-Fi ad LTE rates for each user, with oly oe Wi-Fi beig chose for each user. As a result, we ca calculate the total QoE for ay Wi- Fi associatio so as to check all possible associatios to fid To avoid the exhaustive search, we first propose a greedy algorithm to fid a feasible solutio, which starts with the umber of user is i the system. It places each user ito the system ad fixes them o each Wi-Fi AP. Solve the OPT- for N I times to fid oe feasible solutio that improves the objective fuctio most. The we repeat the search for (N ) I times with the previous coectio fixed. Repeat the iteratios util all the users are assiged. I each iteratio, it is a covex problem ad ca be solved by the covex optimizatio method. Let D be a optimal solutio of the relaxed optimizatio of OPT-. However, we have to discretize the solutio as {D,, D m }. A simple algorithm is quatizig D to the closet value D f that belogs to oe of {D,, D m }, ad

6 6 Z yaw (, ) Y pitch roll X Fig. 3. FoV projectio ad rotatig sphere geometry. Algorithm Heuristic Algorithm with Pealty Fuctio : Covert OPT- to OPT- with pealty fuctio; : Solve OPT- ad get d wifi,i ; 3: Fid the K users (idexed by k) coected to more tha a sigle AP; : for k =, K, k + + do : Use greedy approach oly o these users while fixed others ad update D ; 6: ed for 7: Roud D to appropriate represetatio. j Df,j makig sure that D f satisfies the costrait j D,j, otherwise, lower the level of Df. Note that some badwidth will be wasted after the quatizatio i some cases. If we wat to utilize the wasted badwidth, we eed to further cosider which user ca utilize the badwidth effectively based o their achievable rates o each etwork. We ca allocate rates to the tile which ca improve the objective fuctio most. However, through simulatios, we foud that sice our method is a cetralized method which fully utilizes the resources ad we adopt a large umber of represetatios, the feasible solutio obtaied by quatizig method has already bee very promisig i most cases. This further step ca oly achieve a small promotio with icreasig the complexity. Thus, through this paper, we will just quatize the result without further utilizig the small wasted badwidth. The greedy algorithm is illustrated i Algorithm. B. Heuristic Algorithm with Pealty Fuctio However, the greedy approach still has a complexity of O(N I), which is ot effective as will be evideced i the simulatios. To further reduce the algorithmic complexity ad improve performace, we propose a provably ear-optimal solutio by itroducig a pealty fuctio ito the problem ad relaxig the OPT- ito a covex problem. To be specific, the pealty fuctio is a regularizatio term, which is the square root of the l orm of vectors [d wifi,,, dwifi,i ]. Thus, the problem ca be writte as: OPT- : N Y max (µ mi U(D,j ) + U(D,j )W,j )P y y σ ( ) d wifi,i i s.t. D,j d,, j () d LTE r LTE, () d wifi,i r wifi,i, i, () d, (3) D D,j D m. () where σ is the coefficiet of the pealty fuctio ad we empirically set it as. through this paper. The cardiality card([d wifi,,, dwifi,i ]) = costrait for each user is relaxed as a l orm costrait, i.e., [d wifi,,, dwifi,i ] = i dwifi,i. Note that l orm costrait promotes the sparsity of the vector [d wifi,,, dwifi,i ], ad forces may dwifi,i to be zero. Istead of imposig a umber of strict costraits, we pealize the Euclidea orm of the l orm, which ca collectively force [d wifi,,, dwifi,i ] to be sparse. I may cases, oly oe ozero elemet that gives a assigmet of user to a specific Wi-Fi i is obtaied. Based o this ew formulatio, we ca get the video rates D,j o each tile of each user if the problem is covex. Thus, we theoretically aalyze the OPT- ad fid the objective fuctio is covex, the set of the costraits are covex ad the Slater coditio is satisfied. It is a covex optimizatio problem which ca be effectively solved by existig covex optimizatio methods [3], []. Notice that the solutio ca potetially make users coect to more tha oe AP after the relaxatio of the costraits. Although some users are still assiged to more tha a sigle AP, we ca fid that due to the pealty fuctio, oly oe elemet of [d wifi,,, dwifi ] is large eough ad the rest are,i

7 7 relatively small. We ca idetify users who are still coected to more tha oe AP with d wifi,i larger tha a small pre-set threshold. After that, we ca use search methods o users with more tha oe assiged APs while keepig the associatios of other users fixed to fid a sub-optimal solutio. The we apply the quatizatio method, same as that of the Algorithm, to discretize the solutio as {D,, D m }. The procedure is summarized i Algorithm. From Algorithm, we ca fid that the complexity of this heuristic algorithm is very low whe compared with the greedy algorithm. Accordig to our simulatios, i a -user system, o more tha 3 users will be assiged to multiple APs after the first step. The algorithm ca be icluded as a module of the multi-ran cotroller, which decides the allocatio ad associatio for several frames based o the predictio. For each iteratio, covergece loop is cotiuously iterated from the previous poit, rather tha from a iitial poit. Thus, the cotroller ca respod to users behaviors quickly with fast covergece. C. Decompositio Algorithm A algorithm that decomposes the problem is proposed here to derive the solutio as quickly as Algorithm without relaxig the discrete strategy space. More specifically, the problem OPT- ca be decomposed ito two optimizatio problems, OPT-3 ad OPT-, as follows: OPT-3 : N max U (d ) s.t. d = d LTE OPT- : max d LTE r LTE d wifi,i r wifi,i + i Y y ( W,j )P y d wifi,i, (), (6), i, (7) card( [ d wifi,,, d wifi,i] ) =. () Y y µ mi U(D,j ) + U(D,j )W,j s.t. D,j d, j (9) D,j {D,, D M }. () OPT-3 combies the saliecy weight ad FoV probability with the chael state iformatio of users, it is aimig to derive the Wi-Fi AP associatio ad rate allocatio for each user. OPT-3 ca be effectively solved by applyig pealty fuctio-based method as Algorithm. The proposed problem OPT- is used to optimize the rate allocatio o each tile Algorithm 3 Decompositio Algorithm : Solve OPT-3, get d ad Wi-Fi AP allocatio; : for =, N, + + do 3: sort the utility over the cost of each tile ν,j m ad set d curret = ; : while d curret d do : update the represetatio level to each tile accordig to the utility over the cost cotiuously ; 6: update d curret = d curret + d cosumed ; 7: ed while : ed for for a certai user based o the results from OPT-3. We ca solve the kapsack problem OPT- for each user with greedy approach similar to []. By sortig the utility over cost for each tile with probable represetatio D,j {D,, D M } ad cotiuously updatig the represetatios util all rates are cosumed up, we ca get the feasible solutio D,j for each tile of users. The cost fuctio C,j m shows the cost to pay for choosig the m-th represetatio o tile j. After the first represetatio is selected, the algorithm ca improve the represetatio, ad it oly eeds to pay the differece betwee the allocated represetatio ad the ew represetatio. Therefore, it ca be iterated quickly if we wat to update the represetatio with more badwidth/rates. The cost is defied as: { D C,j m m =,j D m,j, m D,j m, m = () The utility gai is defied as: { U Ũ(D,j)) m m =,j U m,j, m U,j m, m = () The utility over cost deotes the utility gai whe m-th represetatio is selected for tile j of each user per cost, which is defied as: ν,j m Ũ(D,j m = ))W,j C,j m /r, (3) ote that the utility over cost defied here is differet from that i []. It is because our system has multiple users, ad differet users have differet extets of ability to utilize the badwidth. The decompositio algorithm is summarized i Algorithm 3. Although the algorithm decomposes the origial problem OPT-, it allows us to use discrete strategy space without relaxatio. The results are also promisig i some scearios. What is more, the iteratio i this algorithm ca also coverge quickly from curret result ad is useful for updatig the buffer which will be discussed i ext sectio. Note that, if the server caot utilize a feedback lik to get the users behavior iformatio to obtai predict the FoV, all the three algorithms are still applicable by usig broadcastig. By applyig our algorithms without FoV predictio ad FoV probability results, the server ca broadcast each video to users just accordig to the saliecy weight.

8 B B C B Server New frames Feedback Istead of oly usig adaptive rate allocatio for the subsequet optimizatio, we itegrate our trasmissio algorithms with buffer-based scheme to achieve better QoE. Iformally, we should make the rate selectio more coservative ad lower dow the rate for updatig the frames i the buffer whe the buffer is at risk of uderruig. O the other had, more aggressive rate selectio ad more rates o updatig whe the buffer is close to full. Thus, the rate we ca allocate for the subsequet optimizatio is i proportio to the estimated throughput ad curret buffer size d subsequet = f(d estimated, B c ) [], [6]. I this paper, we deote the proportioal relatioship as follows: Curret View Playout Fig.. A hierarchical updatig buffer. V. BUFFER MANAGEMENT STRATEGY Without a playback buffer, a user may see a froze/blak scree ad has to wait for the arrival of the subsequet video frames, if we caot estimate the states accurately uder a time-varyig chael coditio. This would result i poor QoE. However, as illustrated i Sectio I, buffer maagemet schemes for traditioal videos are ot appropriate for 36-degree videos due to the short-time predictio ature of FoV. To save badwidth ad attai high QoE, the FoVdrive schemes ca oly be applied i a short time from curret view (about s) [3], while a robust buffer to avoid the re-bufferig evets requires a relatively log buffer legth. Therefore, i this sectio, we propose a ovel hierarchical buffer updatig strategy, built upo our proposed resource allocatio algorithms, to solve this problem for FoV-drive 36-degree videos. The hierarchical updatig buffer is show i Figure, where B is the buffer legth threshold that is set accordig to the lowest accuracy the server ca accept. Therefore, the predictio-based scheme is acceptable oly if the the buffer has less tha B frames. If ot, it is hard to predict the behavior of users ad the server eeds to trasmit all the tiles equally. B is the maximum buffer legth which is set to avoid the re-bufferig evets i time-varyig etworks. We ca adopt trasmissio schemes with our proposed algorithms for frames durig [, B ] i the buffer, while oly streamig the low represetatios equally for all tiles of the frames durig [B, B ]. However, whe the frames based o the predictio are cosumed up, users will see the low-quality frames eve if the chael is good eough for higher quality frames. To solve this problem, we ca update the qualities of tiles with low represetatio if we have more badwidth for updatig ad the frames i the buffer are sufficiet to avoid the re-bufferig evets. Thus, the rate/badwidth we ca allocate for subsequet optimizatio is decomposed ito two parts, oe is used for updatig the arrivig tiles for playout to maximize the QoE, ad the other is used for storig more frames to miimize the probability of re-bufferig evets. d subsequet = l destimated B B c, () where l is the proportioal coefficiet, which ca be decided by sever as []. Our hierarchical buffer updatig strategy for 36-degree VR videos ca be summarized as follows: If the curret buffer size satisfies B c < B, the buffer is at a risk of ruig out. We use Algorithm or 3 to trasmit frames durig [, B ]. I this step, we oly trasmit the whole frames durig [B c, B ]. For those frames durig [, B c ], we oly update those tiles that eed a higher level represetatio based o the results ad keep usig other tiles already i the buffer, while the rates are equally allocated to tiles for the frames after B. If the curret buffer size satisfies B c > B, the buffer is cosidered full eough. We will first use Algorithm or 3 to update the tiles durig [, B ]. The we trasmit the tiles with equal rates for frames after B c, util all estimated rates are cosumed up. This strategy ca esure eough tiles i the buffer ad always update the upcomig playout tiles if there are eough rates for allocatio. It makes a good trade-off betwee updatig existig frames ad dowloadig ew frames. Besides, the maximum buffer legth is a bit smaller tha traditioal video buffer to reduce the ifluece of bad predictio i a loglegth buffer. I future work, we will ivestigate the optimal size of B ad B to achieve better performace. Sice MPEG (Movig Picture Experts Group) has already supported the tilig scheme i the Media Presetatio Descriptio (MPD) file [3], our buffer scheme ca be easily applied to the system. By specifyig spatial relatioship descriptio (SRD) i the MPD file, the server ca trasmit certai tiles with certai rates to users [7]. VI. SIMULATIONS I this sectio, the effectiveess ad favorable performace of our proposed immersive media trasmissio scheme is validated via simulatios. Overall, the achieved utilities of our proposed algorithms are much higher tha other bechmark techiques. The amout of improvemet i resource allocatio is evaluated with badwidth ad umber of users i the system. The performace of buffer strategy is evaluated through a time-varyig etwork. We begi with describig the simulatio setup used i the later evaluatios.

9 9 A. Simulatio Setup TABLE II VIDEOS USED FOR EVALUATION Name AerialCity Balboa BraCastle Broadway ChairliftRide Divig with sharks DrivigICity DrivigICoutry Gaslamp Harbor KiteFlite Ladig Polevault SkateboardILot SkateboardTrick Surrouded by Wild Elephats Trai Trolley Source Quality P P/ P/ P/ P P P/ P To prove the efficiecy of our scheme ad show the geerality, we use distict 36-degree videos from MPEG- JVET (Joit Video Explorig Team) 36-degree VR video datasets ad YouTube (as show i Table. II) to evaluate our proposed scheme. All videos are used equirectagular projectio ad segmeted ito = 3 tiles. The server ca provide differet-bitrate represetatios for each tile: {.,.,,.9, }Mbps (idexed from to ) ad the guarateed probability ρ is set as.9. The frame rate is set as 3fps, ad frames are i oe Group of Pictures (GoP). B ad B are set as s ad s respectively. The 36-degree videos are viewed by users through head-mouted displays (HMD), such as HTC Vives. The FoV is about *9 degrees. As we explaied i Sectio III. A, we assume that the FoV cotais the whole tiles exactly. The umber of tiles i the FoV will be chaged through the positio due to the ERP we used. Thus, i our case the FoV cosists 6 tiles at least ad tiles at most. Video cotets are requested radomly by users, whose motios are recorded ad coverted to predictio results as method i [3]. The trasmissio part is simulated i NS-3, IEEE. Wi-Fi APs ad default LTE parameters are used i the module. The users are uiformly distributed aroud Wi-Fi APs withi the coverage of LTE BS withi m. Whe more users are ivolved ito the system, they will be set close to AP to simulate the cogestio sceario. All users ca access ay oe of the Wi-Fi APs ad LTE at the same time ad their achievable rates ca be calculated based o positios ad chael quality iformatio. Total badwidth ca be chaged from MHz to 7MHz. To prove the superior performace, we compare our scheme (Cetralized + Probable FoV + Algorithm, Cetralized + Probable FoV + Algorithm ad Cetralized + Probable FoV + Algorithm 3) with the followig competig schemes: ) Cetralized + Oe FoV + Algorithm : It utilizes the heterogeeous etwork we metioed i Sectio II, which ca cetralized cotrol the resource to trasmit the videos. However, oly the most probable FoV is take ito cosideratio without predictio accuracy (FoV probability results). ) Decetralized + Probable FoV: It cosiders all the factors as our proposed method except it optimizes the LTE ad WLAN resource separately i a decetralized etwork. 3) Cetralized + Probable FoV + Exhaustive Search: It utilizes our heterogeeous etwork to trasmit the videos. However, it tests all possible Wi-Fi AP associatios ad optimizes resource allocatio for each. ) Cetralized + Probable FoV + Equal Rate Allocatio: It utilizes our heterogeeous etwork to trasmit the videos. However, it allocates rates equally i the FoV ad igores the saliecy cotributio. ) Short Buffer-Based Strategy: It utilizes our Algorithm i streamig, but the buffer is oly s legth. Therefore, the predictio accuracy of FoV is acceptable for all frames i the buffer. 6) Log Buffer-Based Strategy: It is similar to short buffer-based scheme, whereas the buffer size is s. The predictio accuracy of FoV decreases with the icrease of cotets i buffer. For all the frames with predictio accuracy is, the server will trasmit equal rates to all tiles. B. Simulatio Results Figure illustrates the utility of all methods with respect to the available badwidth ad umber of users. We ca clearly see that our proposed scheme performs much better tha others, especially the performace of our scheme with Algorithm is very close to that of the exhaustive method. Our scheme with Algorithm 3 performs well whe the badwidth is small as show i Figure (a). However, it separates the problem ad tries to fetch the badwidth first for users. As a result, the performace is ot so promisig whe there is eough badwidth to improve the quality i the FoV. Besides, our scheme with Algorithm also performs well util the umber of users icreases to 3 (see Figure (b)), where cogestio starts to occur o Wi-Fi AP. It is worthwhile to poit out that the total utility (QoE) begis to drop because the server wats to esure every user get at least a lowest represetatio. Users caot get eough rates o high saliecy parts. Cosequetly, the total QoE decreases. Our scheme with heuristic algorithm drops (util the umber of users icreases to ) later tha others, sice it ca cosider the saliecy, FoV predictio ad users chael states collectively. As expected, decetralized method performs badly because it utilizes the two etworks separately. Whe there is a cogestio i oe of the etwork, the server caot allocate the resource effectively. The simulatio results also reveal that saliecy is a sigificat factor to ifluece the QoE whe users caot get eough rates. Whe there are users i the system, the utility of equal rate allocatio method is eve worse tha decetralized method. I such sceario, each user ca oly get small rates due to the cogestio, the equal allocatio method wastes the limited resource o those lower saliecy parts, resultig i

10 Total Utility FoV FoV Cetralized+Probable FoV+Algorithm Decetralized+Probable FoV Cetralized+Oe FoV +Algorithm Cetralized+Probable FoV+Equal Cetralized+Probable FoV+Algorithm Cetralized+Probable FoV+Algorithm 3 Cetralized+Probable FoV+Exhaustive Search (a) 7 Badwidth (MHz) (a) Total Utility 3 7 (b) Cetralized+Probable FoV+Algorithm Decetralized+Probable FoV Cetralized+Oe FoV +Algorithm Cetralized+Probable FoV+Equal Cetralized+Probable FoV+Algorithm Cetralized+Probable FoV+Algorithm 3 Cetralized+Probable FoV+Exhaustive Search Fig. 6. (a) The saliecy ad FoV example. (b) The performace of 36-degree video. User Number (b) TABLE III T HE REPRESENTATION LEVEL OF EACH TILE IN F IGURE a poor utility. O the other had, whe there are less users or more badwidth, equal rate allocatio becomes better tha decetralized method. Impact of Saliecy ad FoV: We demostrate the video performace with our scheme (Heterogeeous + Probable FoV + Algorithm ) i Figure 6. Figure 6(a) shows the saliecy map ad two exemplar FoVs amog all probable FoVs from the predictio of oe user. Figure 6(b) demostrates the video quality of all tiles. It ca be see that FoV has the highest quality due to the high probability ad high saliecy weight. Although FoV has a similar viewig probability as FoV, we ca fid FoV cotais more tiles sice it is close to the pole o the sphere. Besides, the levels of represetatios allocated to FoV are smaller tha o FoV as show i Table III. It is because that the saliecy weight i FoV is much larger tha that i FoV. By cotrast, eve tile 6 has a low saliecy weight, due to the QoE metric used, the rate Represetatio Level Fig.. The performace of differet methods. (a) Utility with badwidth. (b) Utility with umber of users. 6 User Idex Tile Idex Fig. 7. Rate allocatio of each tile o each user. differece i the FoV will ot be big. Sice users are more likely to be attracted by the high saliecy object (e.g., the elephat) i the FoV, it is hard to detect the visual discrepacy with small rate differece as show i Figure 6(b). There is aother elephat we ca see o the left of the D scree. Despite the high saliecy o that, it is ot i the predicted FoV, resultig i a lowest rate. What is iterestig, tiles - s rates are much lower tha others. However, most users ca still ejoy the video eve they are viewig FoV, it is hard to detect the visual discrepacy sice they will ot put more attetio o the low saliecy object (e.g. the sky). Impact o Differet Users: Detailed rate allocatio o each user is depicted i Figure 7. Although all the saliecy is

11 7 Curret View Throughput Chages sec 3 Normalized Utility (a). Represetatio Level Throughput (bps) Hierarchical Buffer Upadatig Strategy Short Buffer-based Strategy Log Buffer-based Strategy 6 sec 3 GoP Idex (b) Tile Idex Fig. 9. Buffer details with hierarchical buffer updatig strategy. Fig.. (a) The time-varyig etwork (b) Utility of differet buffer strategies i the time-varyig etwork. VII. C ONCLUSION ormalized for each 36-degree video, the user ca still look at distict kids of views ad their behaviors are differet. The achievable rates of users ad are very close ad both are lookig at the same video. However, they are lookig at differet FoVs, therefore user gets higher rate due to the ifluece of the saliecy ad FoV as show i Figure 7. Buffer Strategy: Figure (a) shows the chael fluctuatio i the etwork. I such sceario, we assume that we oly have the curret chael states iformatio of the etwork. However, we do ot have a estimatio of future states. The server adopts buffer strategy always accordig to the curret states. Figure (b) demostrates the utility of oe user alog the etwork fluctuatio with two bechmark buffer strategies. We ca fid that our hierarchical buffer updatig strategy performs much better tha others. Short-buffer strategy ca achieve high quality whe the buffer is ot empty. However, it is easy to pause ad re-bufferig whe the performace of etwork becomes bad. Besides, whe the etwork recovers, it takes loger time tha our hierarchical buffer updatig strategy to get a high utility sice there is o updatig. By cotrast, log-buffer strategy ca avoid the re-bufferig evets effectively. The curret buffer size is always larger tha B. Noetheless, the cost is that it always keeps a low quality (eve worse tha short-buffer strategy sometimes) sice the poor predictio i the buffer s area after B. Our hierarchical updatig strategy ca achieve a good tradeoff betwee the predictio accuracy ad the buffer size, as a result, it achieves a high average utility. The detailed iformatio of bufferig with our hierarchical buffer updatig strategy is demostrated i Figure 9. Here we just show oe of the frame i each GoP due to the limit of space. We ca fid that the levels of represetatios of some tiles are high i curret view ad upcomig frames, while it is relatively low after B. Those high level represetatio tiles are likely to be FoV accordig to the high accuracy predictio. Thus, our strategy ca make users FOV a high quality whe the badwidth is eough as well as avoid the buffer beig empty whe the performace of etwork turs bad. I this paper, we propose a tile-based 36-degree VR video trasmissio scheme ad a correspodig buffer strategy o heterogeeous etworks with multi-user access. To better improve the experiece of users, we joitly cosider saliecy i videos, filed of view ad the chael quality states of users. The proposed scheme adaptively chooses the most appropriate Wi-Fi AP coectio ad allocates heterogeeous LTE/WLAN resources at the same time for each tile of each user. Besides, we proposed a highly effective heuristic search algorithm to solve a NP-hard mixed-iteger problem with low complexity. Moreover, a ovel buffer updatig strategy is proposed to tackle the bufferig problem of FoV-drive 36-degree videos. The simulatio results show that our proposed scheme ad algorithms outperform other methods. ACKNOWLEDGMENT This work was supported i part by the Natioal Natural Sciece Foudatio of Chia uder Grat 66, Grat 6673, Grat 66, Grat 66, i part by the Natioal High Techology Research ad Developmet Program (63 Program) uder Grat AA. R EFERENCES [] [] [3] [] [] [6] [7] Cisco visual etworkig idex: Forecast ad methodology, 6, 6. P. Rodao Alface, M. Jea-Fracois, ad V. Nico Iteractive omidirectioal video delivery: a badwidt effective approach, Bell Labs Techical Joural,vol.6, o., pp.3-7, Y. Bao, H. Wu, T. Zhag, A.A. Ramli,ad X. Liu, Shootig a movig target: Motio-predictio-based trasmissio for 36-degree videos, IEEE Iteratioal Coferece o Big Data (Big Data), pp.6-7, 6. A. Ghosh, V. Aggarwal,ad F. Qia, A rate adaptatio algorithm for tilebased 36-degree video streamig, arxiv preprit arxiv:7.., 7. X. Corbillo, G. Simo, A. Devlic, ad J. Chakareski, Viewportadaptive avigable 36-degree video delivery, IEEE Iteratioal Coferece o Commuicatios (ICC), pp. -7, 7. M. Hosseii ad V. Swamiatha, Adaptive 36 VR video streamig: Divide ad coquer, IEEE Iteratioal Symposium o Multimedia (ISM), pp. 7-, 6. S. Kim, H. Lee, D. Jeo, ad S. Lee, Reductio i ecodig redudacy for overlapped FOVs over wireless visual sesor etworks, Digital Sigal Processig,, pp. 3-9, 6.

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