TS360: A Two-Stage Deep Reinforcement Learning System for 360-Degree Video Streaming

360-degree video provides the users with immersive experience of the designated scene. However, the challenge is that delivery of the full high-quality 360-degree videos may exhaust the bandwidth leading to degraded user experience. Moreover, unreasonable number of tiles results in uneven distribution of resources during transmitting. In this paper, we propose a Two-Stage deep reinforcement learning system for 360-degree video streaming, named TS360, which determines the division of tiles and the quality allocation in two successive stages. In the tile division stage, the tiles are divided according to the viewing region weights so that the bandwidth can be utilized more efficiently in the quality allocation stage. On the other hand, we employ the Asynchronous Advantage Actor-Critic (A3C) with the aid of ResNet for optimizing the Quality of Experience (QoE) and the bandwidth efficiency. Simulation results compared with the state-of-the-art benchmarkers indicate that TS360 increases the bandwidth efficiency by 56% while improving the user QoE by 10% on average.