Energy and QoE Optimization for Mobile Video Streaming with Adaptive Brightness Scaling

Abstract

Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context. Their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO , a novel user- P erception-based video E xperience O ptimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between a device’s state of motion, video quality and the resulting user-perceived quality. Specifically, by capturing the motion of the on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function and adapt to user preference, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.