Modeling and Alleviating Low-Battery Anxiety for Mobile Users in Video Streaming Services

The pervasive low-battery anxiety (LBA) among modern mobile users has a negative impact on users' emotion and health, and such anxiety may directly lead to the 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 of mobile users and design a tailored mechanism for LBA alleviation by saving display energy in video streaming. In specific, we first conduct a large-scale survey of 2000+ mobile users and strategically extract an empirical LBA model that captures the variation of users' anxiety degree along with the battery power draining. Then, by exploiting the emerging edge computing paradigm, we propose a novel solution for low-power video streaming services (namely, LPVS) 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. To accommodate the heterogeneous LBA properties of mobile users under different circumstances, we develop an LBA model updating scheme by further considering the specific and local edge environments. The emulation results using real-world video watching traces demonstrate that LPVS can effectively alleviate mobile users' LBA and prolong their video watching time (i.e., customer retention) by 39%. © 2021 IEEE.