BOLA360 : Near-optimal View and Bitrate Adaptation for 360-degree Video Streaming

Recent advances in omnidirectional cameras and AR/VR headsets have spurred the adoption of 360 \(^{\circ}\) videos, which are widely believed to be the future of online video streaming. 360 \(^{\circ}\) videos allow users to wear a head-mounted display (HMD) and experience the video as if they are physically present in the scene. Streaming high-quality 360 \(^{\circ}\) videos at scale is an unsolved problem that is more challenging than traditional (2D) video delivery. The data rate required to stream 360 \(^{\circ}\) videos is an order of magnitude more than traditional videos. Further, the penalty for rebuffering events where the video freezes or displays a blank screen is more severe as it may cause cybersickness. We propose an online adaptive bitrate (ABR) algorithm for 360 \(^{\circ}\) videos called BOLA360 that runs inside the client’s video player and orchestrates the download of video tiles from the server to maximize the quality-of-experience (QoE) of the user. BOLA360 conserves bandwidth by downloading only those video tiles that are likely to fall within the field-of-view (FOV) of the user. In addition, BOLA360 continually adapts the bitrate of the downloaded video tiles so as to enable a smooth playback without rebuffering. We prove that BOLA360 is near-optimal with respect to an optimal offline algorithm that maximizes QoE. Further, we evaluate BOLA360 on a wide range of network and user head movement profiles and show that it provides \(6\%\) to \(110\%\) improvements to the QoE of state-of-the-art algorithms. While ABR algorithms for traditional (2D) videos have been well-studied over the last decade, our work is the first ABR algorithm for 360 \(^{\circ}\) videos with both theoretical and empirical guarantees on its performance.