Harmonizing Energy Efficiency and QoE for Brightness Scaling-based Mobile Video Streaming

Chao Qian, Daibo Liu, Hongbo Jiang
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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, and 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-Perception-based video Experience Optimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between device’s state of motion, BS factor, video bitrate and the resulting user-perceived quality. Specifically, by capturing the motion of 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 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.
协调基于亮度缩放的移动视频流的能效和QoE
亮度缩放(BS)是一种新兴的、有前途的移动视频流节能技术。然而,现有的基于BS的方法完全忽视了BS因素、视频比特率和环境上下文之间固有的交互作用,以及它们对移动场景下用户视觉感知的综合影响,导致能量消耗与用户体验质量(QoE)之间的不协调。本文通过综合考虑设备的运动状态、BS因子、视频比特率和由此产生的用户感知质量之间的内在联系,针对能量受限的移动视频流,提出了一种基于用户感知的视频体验优化算法PEO。具体来说,PEO通过捕捉在运行设备的运动,首先推断出最优的比特率和BS因子,从而在保证用户感知的QoE的同时避免比特率低效节能。在此基础上,我们将设备运动感知和用户感知视频流定义为一个优化问题,提出了一种优化算法来最大化目标函数,从而提出了一种在线比特率选择算法。我们的评估(基于痕量分析和用户研究)表明,与最先进的技术相比,PEO可以将感知质量提高23.8%-41.3%,并节省高达25.2%的能耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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