An Apprenticeship Learning Approach for Adaptive Video Streaming Based on Chunk Quality and User Preference

Abstract

Video traffic has experienced an exponential increase in current years due to the growing ubiquity of mobile equipment and the constant network improvement. To deliver video in high quality across various network conditions, adaptive bitrate (ABR) algorithms dynamically select bitrate for each chunk according to perceived network rate and buffer occupancy. Unfortunately, though ameliorating the quality of chunks with dynamic scenes can obtain more QoE gain than the ones with static scenes, current ABR algorithms generally aim to maximize the average bitrate rather than perceptual quality, resulting in the QoE degradation. To address this issue, we propose a dynamic-chunk quality-aware adaptive bitrate scheme via apprenticeship learning named DAVS (Dynamic-chunk quality Aware Video Streaming), in which higher quality is chosen for the dynamic chunks without decreasing the quality of static chunks excessively. Moreover, we take the user's viewing preference into account to make DAVS adapt to the QoE diversity. The experimental results show that DAVS enhances the quality of dynamic chunks and greatly improves the QoE compared with the state-of-the-art ABR algorithms.