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

This is the authors' abstract. We don't add key points for this paper.

Recent advances in omnidirectional cameras and AR/VR headsets have spurred the adoption of 360 \(∘\) videos, which are widely believed to be the future of online video streaming. 360 \(∘\) 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 \(∘\) videos at scale is an unsolved problem that is more challenging than traditional (2D) video delivery. The data rate required to stream 360 \(∘\) 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 \(∘\) 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 \(∘\) videos with both theoretical and empirical guarantees on its performance.