Using Viewing Statistics to Control Energy and Traffic Overhead in Mobile Video Streaming

Video streaming can drain a smartphone battery quickly. A large part of the energy consumed goes to wireless communication. In this article, we first study the energy efficiency of different video content delivery strategies used by service providers and identify a number of sources of energy inefficiency. Specifically, we find a fundamental tradeoff in energy waste between prefetching small and large chunks of video content: small chunks are bad because each download causes a fixed tail energy to be spent regardless of the amount of content downloaded, whereas large chunks increase the risk of downloading data that user will never view because of abandoning the video.

Hence, the key to optimal strategy lies in the ability to predict when the user might abandon viewing prematurely. We then propose an algorithm called eSchedule that uses viewing statistics to predict viewer behavior and computes an energy optimal download strategy for a given mobile client. The algorithm also includes a mechanism for explicit control of traffic overhead, i.e., unnecessary download of content that the user will never watch. Our evaluation results suggest that the algorithm can cut the energy waste down to less than half compared to other strategies. We also present and experiment with an Android prototype that integrates eSchedule into a YouTube downloader.