On adaptive video streaming with predictable streaming performance
Yan Liu, Jack Y. B. Lee · 2014
Adaptive video streaming is an essential tool for improving the performance of video delivery over mobile networks. By dynamically switching between different bit-rate versions of the same video, adaptive video streaming can compensate for and adapt to the ever-changing network conditions inherent in today's 3G/4G networks. However, existing adaptive streaming algorithms, both academic and commercial ones, do not offer any prediction on the streaming performance of future streaming sessions, nor allow the content providers or users to explicitly control the tradeoff between streaming performance and video quality. This study tackles this fundamental challenge by developing a novel framework called Throughput-Differentiated-Rate-Adaptive Post-Streaming-Rate-Analysis (TDRA-PSRA) based on a new statistical model that directly relates past bandwidth statistics to future streaming performance. Extensive simulation results obtained from real-world mobile network trace data revealed three remarkable properties of TDRA-PSRA: (a) the actual average streaming performance is very close to the target set forth by the ICPs/users; (b) it enables the ICPs/users to control the tradeoff between streaming performance and video quality; (c) it offers a mean to directly control the frequency of bit-rate switches — a key factor to subjective video quality.