Adaptive Streaming of 360 Videos with Perfect, Imperfect, and Unknown FoV Viewing Probabilities in Wireless Networks
Abstract : This paper investigates adaptive streaming of one or multiple tiled 360 videos from a multi-antenna base station (BS) to one or multiple single-antenna users, respectively, in a multi-carrier wireless system. We aim to maximize the video quality while keeping rebuffering time small via encoding rate adaptation at each group of pictures (GOP) and transmission adaptation at each (transmission) slot. To capture the impact of field-of-view (FoV) prediction, we consider three cases o f FoV viewing probability distributions, i.e., perfect, imperfect, and unknown FoV viewing probability distributions, and use the average total utility, worst average total utility, and worst total utility as the respective performance metrics. In the single-user scenario, we optimize the encoding rates of the tiles, encoding rates of the FoVs, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. I n the multi-user scenario, we adopt rate splitting with successive decoding and optimize the encoding rates of the tiles, encoding rates of the FoVs, rates of the common and private messages, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. Then, we separate the challenging optimization problem into multiple tractable problems in each scenario. In the single-user scenario, we obtain a globally optimal solution of each problem using transformation techniques and the Karush-Kuhn-Tucker (KKT) conditions. In the multi-use r scenario, we obtain a KKT point of each problem using the concave-convex procedure (CCCP). Finally, numerical results demonstrate that the proposed solutions achieve notable gains over existing schemes in all three cases. To the best of our knowledge, this is the first work revealing the impact of FoV prediction on the performance of adaptive streaming of tiled 360 videos.
? These studies are orthogonal to our technique, and can be combined with our technique to further improve the quality of experience of end users.
? Using existing adaptive video streaming ecosystem, one tile is downloaded at a time for 360-degree tiled video streaming.
? However, the existing approach select the video quality levels of all the tiles of the same segment all at once, based on bandwidth estimation made at the time of this rate selection.
? When the tiles are actually downloaded, this estimation can change yet the techniques do not allow for any further adaptation.
? We separate the optimization problem into multiple tractable problems which can provide satisfactory performance.
? We obtain globally optimal solutions of the separate optimization problems using transformation techniques and the Karush-KuhnTucker (KKT) conditions in each case.
? The total utility maximization problems of these six baseline schemes are formulated similarly to Problem 7 and solved using a similar separate approach.
? The separate optimization problems of OptER-OptSDMA-pp, OptER-OptSDMA-ip, and BiER-OptSDMA-up are solved similarly using CCCP.
• We propose a formulation with a manageable action space and a short adaptation cycle that works with delayed reward signals.
• Comprehensive evaluations with real network traces show that the proposed method outperforms three tile-based streaming techniques for 360-degree videos.
• In this work, we propose to use RL instead, which is more appropriate for adaptive stream given the need for optimal sequential decisions.
• Because the viewport prediction network and the RL policy are trained off-line, the overhead of the inference on the client is small, and the proposed solution is feasible to run even on a smartphone.
? It is unknown how FoV prediction errors influence the performance of adaptive streaming of tiled 360 videos.
? It is not clear how advanced nonorthogonal transmission schemes can improve the performance of adaptive wireless streaming of tiled 360 videos.
? It is interesting to know how current multi-antenna base stations (BS) can improve performance.
? To capture the impact of FoV prediction, we consider three cases of FoV viewing probability distributions, i.e., perfect, imperfect, and unknown FoV viewing probability distributions, and use the average total utility, worst average total utility, and worst total utility as the respective performance metrics.
|