Enabled by the rapid development of virtual reality hardware and software, 360-degree video content has proliferated. From the network perspective, 360-degree video transmission imposes significant challenges because it consumes 4 6χ the bandwidth of a regular video with the same resolution. To address these challenges, 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, to guarantee the quality of viewing experience, we have to address the random nature of viewer motion. Based on our experimental study of viewer motion (comprising 16 video clips and over 150 subjects), we found the viewer motion can be well predicted in 100~500ms. We propose a machine learning mechanism that predicts not only viewer motion but also prediction deviation itself. The latter is important because it provides valuable input on the amount of redundancy to be transmitted. Based on such predictions, we propose a targeted transmission mechanism that minimizes overall bandwidth consumption while providing probabilistic performance guarantees. Real-data-based evaluations show that the proposed scheme significantly reduces bandwidth consumption while minimizing performance degradation, typically a 45% bandwidth reduction with less than 0.1% failure ratio.

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%.