  User experience of video streaming could be greatly improved by selecting a
high-yet-sustainable initial video bitrate, and it is therefore critical to
accurately predict throughput before a video session starts. Inspired by
previous studies that show similarity among throughput of similar sessions
(e.g., those sharing same bottleneck link), we argue for a cross-session
prediction approach, where throughput measured on other sessions is used to
predict the throughput of a new session. In this paper, we study the challenges
of cross-session throughput prediction, develop an accurate throughput
predictor called DDA, and evaluate the performance of the predictor with
real-world datasets. We show that DDA can predict throughput more accurately
than simple predictors and conventional machine learning algorithms; e.g.,
DDA's 80%ile prediction error of DDA is > 50% lower than other algorithms. We
also show that this improved accuracy enables video players to select a higher
sustainable initial bitrate; e.g., compared to initial bitrate without
prediction, DDA leads to 4x higher average bitrate.
