Energy optimization for mobile video streaming via an aggregate model

Wireless video streaming on smartphones drains a significantly large fraction of battery energy, which is primarily consumed by wireless network interfaces for downloading unused data and repeatedly switching radio interface. In this paper, we propose an energy-efficient download scheduling algorithm for video streaming based on an aggregate model that utilizes user's video viewing history to predict user behavior when watching a new video, thereby minimizing wasted energy when streaming over wireless network interfaces. The aggregate model is constructed by a personal retention model with users' personal viewing history and the audience retention on crowd-sourced viewing history, which can accurately predict the user behavior of watching videos by balancing "user interest" and "video attractiveness". We evaluate different users streaming multiple videos in various wireless environments and the results illustrate that the aggregate model can help reduce energy waste by 20 % on average. In addition, we also discuss implementation details and extensions, such as dynamically updating personal retention, balancing audience and personal retention, categorizing videos for accurate model.