TKN Technical Reports Series

Recently, HTTP-Based Adaptive Streaming has become the de facto standard for video streaming over the Internet. It allows the client to dynamically adapt media characteristics to varying network conditions in order to maximize Quality of Experience, that is, minimize playback interruptions, while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, where buffering possibilities are limited, this task becomes particularly challenging. An important factor than might help improving performance is the capability to correctly predict network throughput dy-namics on short to medium timescales. This problem becomes notably difficult in wireless networks that are often subject to continuous throughput fluctuations. In the present work, we develop an adaptation algorithm for HTTP-Based Adaptive Live Streaming that, for each adaptation decision, maximizes a Quality of Experience based utility function depending on the probability of playback interruptions, average video quality, and the amount of video quality fluctuations. To compute the utility function, in particular the interruption probability, the algorithm leverages throughput predictions, and dynamically estimated prediction accuracy. We are trying to close the gap created by the lack of studies analyzing TCP throughput on shor