A Hybrid Control Scheme for 360-Degree Dynamic Adaptive Video Streaming over Mobile Devices

A 360-degree streaming system can provide immersive, interactive, and autonomous experiences surrounding the user by viewpoint changing to see different angles of the 360-degree video. Due to the limited and highly dynamic cellular network conditions, high-resolution 360-degree video playback over mobile devices often suffer from playback freezing, and inevitable bandwidth waste appears in delivering those out-of-viewpoint parts. In this paper, a hybrid control scheme is presented for segment-level continuous bitrate selection and tile-level bitrate allocation for 360-degree streaming over mobile devices to increase users' quality of experience. First, a deep reinforcement learning (RL) method is proposed to predict the segment bitrate and avoid playback freezing events. Second, a viewpoint-prediction-map-based cooperative bargaining game theory is proposed for bitrate allocation optimization to choose a suitable bitrate for each tile to reduce unreasonable bandwidth waste. The proposed scheme is compared with state-of-the-art approaches over a wide variety of mobile network conditions with multiple viewpoint traces and 360-degree video contents. Experimental results indicate that the proposed method outperforms the state-of-the-art approaches in terms of different experimental objectives over mobile devices.