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Recent advances in omnidirectional cameras and AR/VR headsets have spurredthe adoption of 360-degree videos that are widely believed to be the future ofonline video streaming. 360-degree videos allow users to wear a head-mounteddisplay (HMD) and experience the video as if they are physically present in thescene. Streaming high-quality 360-degree videos at scale is an unsolved problemthat is more challenging than traditional (2D) video delivery. The data raterequired to stream 360-degree videos is an order of magnitude more thantraditional videos. Further, the penalty for rebuffering events where the videofreezes or displays a blank screen is more severe as it may causecybersickness. We propose an online adaptive bitrate (ABR) algorithm for360-degree videos called BOLA360 that runs inside the client's video player andorchestrates the download of video segments from the server so as to maximizethe quality-of-experience (QoE) of the user. BOLA360 conserves bandwidth bydownloading only those video segments that are likely to fall within thefield-of-view (FOV) of the user. In addition, BOLA360 continually adapts thebitrate of the downloaded video segments so as to enable a smooth playbackwithout rebuffering. We prove that BOLA360 is near-optimal with respect to anoptimal offline algorithm that maximizes QoE. Further, we evaluate BOLA360 on awide range of network and user head movement profiles and show that it provides$13.6\%$ to $372.5\%$ more QoE than state-of-the-art algorithms. While ABRalgorithms for traditional (2D) videos have been well-studied over the lastdecade, our work is the first ABR algorithm for 360-degree videos with boththeoretical and empirical guarantees on its performance.

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