Energy-Efficient 360-Degree Video Streaming on Multicore-Based Mobile Devices

360° video streaming becomes increasingly popular on video platforms. However, streaming (downloading and processing) 360° video consumes a large amount of energy on mobile devices, but little work has been done to address this problem, especially considering recent advances in the mobile architecture. Through real measurements, we found that existing systems activate all processor cores during video streaming, which consumes a great deal of energy, but this is unnecessary since most heavy computations in 360° video processing are handled by the hardware accelerators such as hardware decoder, GPU, etc. To save energy, we propose to selectively activate the proper processor cluster and adaptively adjust the CPU frequency based on the video quality. We model the impacts of video resolution and CPU frequency on power consumption, and model the impacts of video features and network effects on Quality of Experience (QoE). With the developed models, we formulate the energy and QoE aware 360° video streaming problem as an optimization problem with the goal of maximizing QoE and minimizing energy. We then propose an efficient algorithm to solve this optimization problem. Evaluation results demonstrate that the proposed algorithm can significantly reduce the energy consumption while maintaining QoE.