2015/05/08 by Junchen Jiang, Vyas Sekar, Jiang, Junchen +3
Computer Science · #Advanced Steganography and Watermarking Techniques #FOS: Computer and information sciences #Image and Video Quality Assessment #Internet Traffic Analysis and Secure E-voting #Networking and Internet Architecture (cs.NI) #cs.NI
paper · pdf · doi:10.48550/arxiv.1505.02056
arxiv created 2015/05/08 · openalex publication_date 2015/05/08 · arxiv updated 2015/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.