Seer: Learning-Based 360$^{\circ }$ Video Streaming for MEC-Equipped Cellular Networks

The increasing popularity of virtual reality and 360-degree video streaming has created a demand for streaming high-quality video content over cellular networks. However, streaming 360-degree videos over cellular networks poses a challenge due to the need for ultra-low latency and high bandwidth. Although viewport adaptive streaming presents a promising solution to reduce the bandwidth requirement of 360 $^{\circ }$ video, it remains challenging to predict the Field of View (FoV) due to the adverse impact of prediction errors on the user's Quality of Experience (QoE). Moreover, bitrate adaptation gets complicated due to 360 $^{\circ }$ videos and the dynamic nature of the cellular network. Therefore, in this paper, we propose an intelligent framework, termed Seer, to fully realize 360 $^{\circ }$ video streaming over cellular networks by leveraging Multi-access Edge Computing (MEC). It incorporates an FoV prediction scheme and a bitrate adaptation policy into the MEC platform. In particular, the FoV prediction scheme uses perceived video features coupled with time-series based viewport prediction to achieve higher prediction accuracy. For an effective bitrate selection in cellular network conditions, a Reinforcement Learning (RL) based bitrate adaptation policy is further employed. The results demonstrate that Seer's FoV prediction scheme delivers higher prediction accuracy, while the streaming framework significantly reduces the bandwidth requirements and improves the user's QoE.