Viewing 360 degree videos: Motion prediction and bandwidth optimization

Abstract

360-degree video transmission consumes 4∼6× the bandwidth of a regular video, and thus imposes significant challenges to networks. To address this challenge, in this paper, we propose a motion-prediction-based transmission mechanism that matches network video transmission to viewer needs. Ideally, if viewer motion is perfectly known in advance, we could reduce bandwidth consumption by 80%. Practically, however, we have to address the random nature of viewer motion, in order to guarantee the quality of the viewing experience. Based on our experimental study of viewer motion (comprising 16 video clips and over 150 subjects), we propose a machine learning mechanism that predicts viewer motion. Based on such predictions, we propose a partial-content-transmission mechanism that reduces the overall bandwidth consumption while providing probabilistic performance guarantees. Real-trace-based evaluations show that the proposed scheme significantly reduces bandwidth consumption with negligible performance degradation. For example, given a failure ratio of 0.1%, we can reduce bandwidth consumption by more than 40%.