A Content-Aware Bitrate Selection Method Using Multi-Step Prediction for 360-Degree Video Streaming

Abstract: A content-aware multi-step prediction control (CAMPC) algorithm is proposed to determine the bitrate of 360-degree videos, aiming to enhance the quality of experience (QoE) of users and reduce the cost of video content providers (VCP). The CAMPC algorithm first employs a neural network to generate the content richness and combines it with the current field of view (FOV) to accurately predict the probability distribution of tiles being viewed. Then, for the tiles in the predicted viewport which directly affect QoE, the CAMPC algorithm utilizes a multi-step prediction for future system states, and accordingly selects the bitrates of multiple subsequent steps, instead of an instantaneous state. Meanwhile, it controls the buffer occupancy to eliminate the impact of prediction errors. We implement CAMPC on players by building a 360-degree video streaming platform and evaluating other advanced adaptive bitrate (ABR) rules through the real network. Experimental results show that CAMPC can save 83.5% of bandwidth resources compared with the scheme that completely transmits the tiles outside the viewport with the Dynamic Adaptive Streaming over HTTP (DASH) protocol. Besides, the proposed method can improve the system utility by 62.7% and 27.6% compared with the DASH official and viewport-based rules, respectively.

Keywords: DASH; content-aware FOV prediction; bitrate adaptation; multi-step prediction; generalized predictive control