TVG-Streaming: Learning User Behaviors for QoE-Optimized 360-Degree Video Streaming

Abstract

360-degree video streaming shows great potential to revolutionize the streaming market, by providing much better immersive experience than standard video streams. However, its wide adoption is hindered by the surging demand of network bandwidth due to multi-screen video transmission. To reduce the bandwidth cost, one promising approach is to predict a user’s field of view (FoV), and then prefetch video tiles that a user will view a few seconds ahead. The challenge lies in that user behaviors cannot be properly captured with very limited information, especially the viewing time spent on each tile and the FoV switching behavior are hard to predict. In this paper, we propose a novel 360-degree video streaming algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TVG-Streaming</i> to optimize user experiences by learning user view behaviors. Different from previous approaches, our idea is to exploit tile-view graphs (TVGs) generated by real user behaviors and accurately estimate the probability that each tile falls in the FoV. With the tile view probability, we can determine the bitrate of each tile for delivery and buffering with limited bandwidth budget so as to maximize users’ quality of experience (QoE). For evaluation, we conduct extensive experiments using real traces and the results show that our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TVG-Streaming</i> algorithm significantly outperforms other algorithms by at least 20% improvement in terms of users’ QoE.