Video streaming on mobile devices necessitates a balance between Quality of Experience (QoE) and energy consumption. Given large data sizes from video, extensive amounts of battery power are required for downloading, processing, and playing each video segment. However, video quality may suffer drastically in an effort to pursue energy efficiency. In balancing these two objectives, our research incorporates advanced energy models, processor clock rates, buffer management, and network quality aware downloading. Furthermore, multi-user systems present unique challenges because the availability of network resources can vary greatly over time. We introduce an algorithm that accounts for these fluctuations and effectively balances QoE and energy consumption. Present application in LTE networks and foundations for future implementation in 5G systems are provided. We provide comparison of the performance of our algorithm with an optimal solution and show superior performance against state-of-the-art DASH-inspired algorithms. Finally, we show our algorithm gains in both QoE and energy metrics across users in large-scale multi-cell scenarios.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Networks and Communications
- Electrical and Electronic Engineering