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 means of viewpoint changes to see different angles of a 360-degree video. However, due to the limited capacity and highly dynamic conditions of cellular networks, high-resolution 360-degree video playback over mobile devices often suffers from playback freezing, and bandwidth waste is inevitably incurred in delivering out-of-view video data. 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. 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 under a wide variety of mobile network conditions with multiple viewpoint traces and 360-degree video contents. The experimental results indicate that the proposed method outperforms the compared state-of-the-art approaches in terms of various experimental objectives on mobile devices.