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

Video traffic has experienced an exponential increase in current years due to the growing ubiquity of mobile equipment and the constant network improvement. Most commercial players employ adaptive bitrate (ABR) algorithms to dynamically choose bitrate for each chunk based on perceived network capacity and buffer occupancy. Unluckily, even though improving the quality of chunks with dynamic scenes can achieve more QoE gain than static scenes, current ABR algorithms usually strive to maximize the average bitrate instead of perceptual quality, leading to the QoE degradation. To overcome this obstacle, we introduce a dynamic-chunk quality-aware adaptive bitrate algorithm through apprenticeship learning called DAVS (Dynamic-chunk quality Aware Video Streaming), where higher quality is selected for the dynamic chunks without reducing the quality of static chunks extravagantly. Furthermore, we take the user's viewing preference into account to make DAVS adapt to the QoE diversity. The experimental results demonstrate that DAVS ameliorates the quality of dynamic chunks and significantly enhances the QoE compared with several representative ABR algorithms.

Automated summary

Video traffic has been growing exponentially due to the increasing popularity of mobile devices and network improvements. Most commercial players use adaptive bitrate algorithms to choose bitrates based on network capacity and buffer occupancy. However, current algorithms prioritize average bitrate over perceptual quality, resulting in a degraded Quality of Experience (QoE). To tackle this issue, we propose DAVS (Dynamic-chunk quality Aware Video Streaming), an adaptive bitrate algorithm that employs apprenticeship learning to select higher quality for dynamic chunks without sacrificing static chunk quality excessively. Additionally, DAVS takes into account user viewing preferences to adapt to QoE diversity. Experimental results demonstrate that DAVS improves the quality of dynamic chunks and significantly enhances QoE compared to other representative algorithms.