Alleviating Low-Battery Anxiety of Mobile Users via Low-Power Video Streaming

Guoming Tang, Kui Wu, Deke Guo, Yi Wang, Huan Wang
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引用次数: 5

Abstract

The pervasive low-battery anxiety (LBA) among modern mobile users has caused negative impacts on users’ emotion and health, and such anxiety may directly lead to loss of customers in power-hungry applications, e.g., video streaming. Despite its importance, LBA has not been thoroughly investigated due to the difficulty in quantitatively measuring LBA. To fill the gap, we present a quantitative model to measure the LBA among mobile users and design a tailored mechanism to alleviate it via display energy saving in video streaming. In specific, we first conduct a large-scale user survey among 2000+ mobile users and strategically extract an empirical LBA model that captures the variation of user’s anxiety degree along with the battery power draining. Then, by exploiting the emerging edge computing paradigm, we propose LPVS, a novel solution for low-power video streaming service at the network edge. It aims to minimize the LBA of mobile users, by integrating the extracted LBA model with the energy-saving image/video content transforming techniques. The emulation results using real-world video watching traces demonstrate that, LPVS can effectively alleviate mobile users’ LBA and prolong the low-battery users’ video watching time (i.e., customer retention) by 39%.
通过低功耗视频流缓解移动用户的低电量焦虑
现代移动用户普遍存在的低电量焦虑(low-battery anxiety, LBA)对用户的情绪和健康造成了负面影响,这种焦虑可能会直接导致视频流等耗电应用的客户流失。尽管它很重要,但由于难以定量测量,LBA尚未得到彻底的研究。为了填补这一空白,我们提出了一种量化模型来衡量移动用户的LBA,并设计了一种定制的机制,通过视频流中的显示节能来缓解它。具体而言,我们首先对2000多名移动用户进行了大规模的用户调查,并策略性地提取了一个经验LBA模型,该模型捕捉了用户焦虑程度随电池电量消耗的变化。然后,通过利用新兴的边缘计算范式,我们提出了LPVS,一种新的解决方案,用于网络边缘的低功耗视频流服务。通过将提取的LBA模型与节能的图像/视频内容转换技术相结合,将移动用户的LBA最小化。使用真实视频观看轨迹的仿真结果表明,LPVS可以有效缓解移动用户的LBA,将低电量用户的视频观看时间(即客户留存率)延长39%。
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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