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Recently, HTTP-Based Adaptive Streaming has become the de facto standard forvideo streaming over the Internet. It allows the client to adapt mediacharacteristics to varying network conditions in order to maximize Quality ofExperience (QoE). In the case of live streaming this task becomes particularlychallenging. An important factor than might help improving performance is thecapability to correctly predict network throughput dynamics on short to mediumtimescales. It becomes notably difficult in wireless networks that are oftensubject to continuous throughput fluctuations.  In the present work, we develop an adaptation algorithm for HTTP-BasedAdaptive Live Streaming that, for each adaptation decision, maximizes aQoE-based utility function depending on the probability of playbackinterruptions, average video quality, and the amount of video qualityfluctuations. To compute the utility function the algorithm leveragesthroughput predictions, and dynamically estimated prediction accuracy.  We are trying to close the gap created by the lack of studies analyzing TCPthroughput on short to medium timescales. We study several time seriesprediction methods and their error distributions. We observe that Simple MovingAverage performs best in most cases. We also observe that the relativeunderestimation error is best represented by a truncated normal distribution,while the relative overestimation error is best represented by a Lomaxdistribution. Moreover, underestimations and overestimations exhibit a temporalcorrelation that we use to further improve prediction accuracy.  We compare the proposed algorithm with a baseline approach that uses a fixedmargin between past throughput and selected media bit rate, and an oracle-basedapproach that has perfect knowledge over future throughput for a certain timehorizon.

Low-Delay Adaptive Video Streaming Based on Short-Term TCP Throughput Prediction