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User experience of video streaming could be greatly improved by selecting ahigh-yet-sustainable initial video bitrate, and it is therefore critical toaccurately predict throughput before a video session starts. Inspired byprevious studies that show similarity among throughput of similar sessions(e.g., those sharing same bottleneck link), we argue for a cross-sessionprediction approach, where throughput measured on other sessions is used topredict the throughput of a new session. In this paper, we study the challengesof cross-session throughput prediction, develop an accurate throughputpredictor called DDA, and evaluate the performance of the predictor withreal-world datasets. We show that DDA can predict throughput more accuratelythan simple predictors and conventional machine learning algorithms; e.g.,DDA's 80%ile prediction error of DDA is > 50% lower than other algorithms. Wealso show that this improved accuracy enables video players to select a highersustainable initial bitrate; e.g., compared to initial bitrate withoutprediction, DDA leads to 4x higher average bitrate.

DDA: Cross-Session Throughput Prediction with Applications to Video Bitrate Selection