Motion-Prediction-Based Multicast for 360-Degree Video Transmissions

360-degree video is on the cusp of going mainstream. Such videos have a large size (4-5⌉ the size of a regular one). Since each viewer has to wear Head-Mounted Display (HMD), screen sharing is impossible when a group is watching the same content. Multiple parallel video-streams will be required to serve a group of viewers along the last mile (think of a family or a group of friends watching Super Bowl in their HMDs). This would end up quickly choking the entire network. In this paper, we present a scheme to optimize the network bandwidth using motion-prediction-based multicast to serve concurrent viewers. Based on empirical evaluation of more than 150 viewers watching our pool of sixteen 360-degree videos, we observe that most viewers follow similar motion patterns when watching the same video. We present a data- driven scheme for temporal prediction of viewer motion from previous states, and hence optimize the multicast bandwidth consumption by sending only the portion likely to be watched by a group of viewers. Our evaluations with real viewer motion traces show a bandwidth saving of over 50%, compared to full frame video multicast, and significant bandwidth reduction compared to unicast.