Online Bitrate Selection for Viewport Adaptive 360-Degree Video Streaming

360-degree video streaming provides users with immersive experience by letting users determine their field-of-views (FoVs) in real time. To efficiently utilize the limited bandwidth resources, recent works have proposed a viewport adaptive 360-degree video streaming model by exploiting the bitrate adaptation in spatial and temporal domains. In this paper, under this video streaming model, we propose an online bitrate selection algorithm to enhance the user’s quality of experience (QoE). This is achieved by characterizing the user’s personalized FoV and real-time downloading capacity in an online fashion. We address the unknown user-specific FoV by introducing the reference FoV and design an online bitrate selection algorithm to learn the difference between the user’s actual FoV and the reference FoV. We prove that as the number of video segments increases, the performance of the proposed online algorithm approaches the optimal performance asymptotically, with a bounded error. We perform trace-driven simulations with real-world datasets. Simulation results show that under the scenario where the available video bitrates are relatively high, our proposed algorithm can improve the user’s viewing quality level between
$4.2\!-\!29.4$
percent and reduce the average intra-segment quality switch by at least 12.4 percent when compared with several existing methods.