Download or read book Energy and Quality Aware Video Streaming on Mobile Devices written by Xianda Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Video streaming on mobile devices has seen tremendous growth, and is a major marketing point of many wireless carriers. Because videos have very large data sizes, the mobile devices must spend a great deal of their battery power on downloading and processing the video over the wireless network. Moreover, as 360-degree video which provides immersive experience to users becomes more and more popular, it is more challenging to support 360-degree video streaming on mobile devices considering the bandwidth and energy constraints. The goal of this dissertation is to address these research challenges by developing novel techniques to save energy while maintaining users' Quality of Experience (QoE) during video streaming. First, we propose to save energy by considering the environment (context) of video streaming on mobile devices, i.e., the perception of video quality is different when watching videos at different context (e.g., inside a moving vehicle or at home). In mobile context, we propose to reduce the video quality (i.e., video bitrate) for video streaming, which can significantly reduce the energy consumption without degrading the QoE too much. To model the impact of context, we exploit the accelerometer embedded in mobile devices to measure the vibration level during video streaming. Based on quality assessment experiments, we 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 an optimization problem and propose an efficient algorithm to solve it. Second, we propose techniques to save bandwidth for 360-degree video streaming on mobile devices. Tile-based streaming techniques have been widely used to save bandwidth in 360-degree video streaming. However, it is a challenge to determine the right tile size which directly affects the bandwidth usage. To address this problem, we propose to encode the video by considering the viewing popularity, where the popularly viewed areas are encoded as macrotiles to save bandwidth. We propose techniques to identify and build macrotiles, and adjust their sizes considering practical issues such as head movement randomness. To support popularity aware 360-degree video streaming, we design algorithms to help clients select the right tiles with the right quality level to maximize the QoE under bandwidth constraint. Third, we extend the macrotile based approach to save energy for 360-degree video streaming. In existing research, downloading and processing many small tiles in 360-degree video consumes a large amount of energy on mobile devices. To address this issue, we exploit the idea of macrotiles. When encoding macrotiles, we propose to further save energy by reducing the insignificant frames in each video segment, i.e., reducing the frame rate to save energy while satisfying some QoE constraint. Based on real video traces, we model the impact of video features and user behavior on QoE, and model the impact of video features on power consumption. Based on the QoE model and the power model, we formulate the energy-efficient and QoE-aware 360-degree video streaming problem as an optimization problem, and propose a control theory based algorithm to solve it. Finally, energy can be saved by 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 large amount of energy, but this is unnecessary since most heavy computations in 360-degree video processing can be handled by the hardware accelerators such as hardware decoder, GPU, etc. To address this problem, we propose to save energy by selectively activating the proper processor cluster and adaptively adjusting the CPU frequency based on the video quality. We model the impact of video resolution and CPU frequency on power consumption, and model the impact of video features and network effects on QoE. Based on the QoE model and the power model, we formulate the energy and QoE aware 360-degree video streaming problem as an optimization problem. We first present an optimal algorithm which can maximize QoE and minimize energy. Since the optimal algorithm requires future knowledge, we then propose a heuristic based algorithm. Evaluation results show that our heuristic based algorithm can significantly reduce the energy consumption while maintaining QoE.