LAUSR

Fueled by the growth of 3G/4G mobile networks, mobile video streaming has become one of the main applications in the mobile Internet. Due to mobile networks’ inherent bandwidth fluctuations, the industry as well as researchers have developed many adaptive streaming algorithms to compensate for such fluctuations to improve streaming performance. Given the wide range of network settings, it is not surprising that existing algorithms can and do perform differently across different network and system conditions. This work breaks away from the conventional one-size-fits-all approach to designing adaptive streaming systems by developing a new framework called PSRA where past throughput trace data - captured as a by-product of streaming, are analyzed to construct a statistical model to automatically tune the adaptation algorithm for future streaming sessions according to the underlying network and system configurations. Compared to existing approaches, the PSRA-optimized streaming algorithm can achieve predictable, consistent, and controllable streaming performance across a wide-range of network and system configurations. Moreover, PSRA offers to service provider a new tool to precisely control the tradeoff between video quality and streaming performance. Results from extensive trace-driven simulations as well as experiments verified PSRA's performance under real-world mobile network and system configurations.