Context-Aware and Energy-Aware Video Streaming on Smartphones

High quality video streaming for mobile devices implies high energy consumption due to the transmitted data and the variation of wireless signals. As an example, transmissions in mobile scenarios (e.g., inside a moving bus) consumes more energy for devices than when accessing from a static environment (e.g., at home). The QoE for the user does not substantially increase when watching high bitrate videos in a vibrating environment (i.e., a moving vehicle), as the context, in this case vehicle's vibration, affects the perceived QoE. To address this problem, we propose to save energy by considering the context (environment) of video streaming. To model the impact of context, we exploit the embedded accelerometer in smartphones to record the vibration level during video streaming. Based on quality assessment experiments, we collect traces and model the impact of video bitrate and vibration level on QoE, and model the impact of video bitrate and signal strength on power consumption. Based on the QoE model and the power model, we formulate the context-aware and energy-aware video streaming problem as an optimization problem. We present an optimal algorithm which can maximize QoE and minimize energy. Since the optimal algorithm requires perfect knowledge of future tasks, we propose an online bitrate selection algorithm. To further improve the performance of the online algorithm, we propose a crowdsourcing based bitrate selection algorithm. Through real measurements and trace-driven simulations, we demonstrate that the proposed algorithms can significantly outperform existing approaches when considering both energy and QoE.