Bandwidth-aware Prefetching for Proactive Multi-video Preloading and Improved HAS Performance

Vengatanathan Krishnamoorthi, Niklas Carlsson, Derek Eager, Anirban Mahanti, and Nahid Shahmehri


Paper: Vengatanathan Krishnamoorthi, Niklas Carlsson, Derek Eager, Anirban Mahanti, and Nahid Shahmehri "Bandwidth-aware Prefetching for Proactive Multi-video Preloading and Improved HAS Performance", Proc. ACM International Conference on Multimedia (ACM MM), Brisbane, Australia, Oct. 2015. (pdf)

Abstract: This paper considers the problem of providing users playing one streaming video the option of instantaneous and seamless playback of alternative videos. Recommendation systems can easily provide a list of alternative videos, but there is little research on how to best eliminate the startup time for these alternative videos. The problem is motivated by services that want to retain increasingly impatient users, who frequently watch the beginning of multiple videos, before viewing a video to the end. We present the design, implementation, and evaluation of an HTTP-based Adaptive Streaming (HAS) solution that provides careful prefetching and buffer management. We also present the design and evaluation of three fundamental policy classes that provide different tradeoffs between how aggressively new alternative videos are prefetched versus the importance of ensuring high playback quality. We show that our solution allows us to reduce the startup times of alternative videos by an order of magnitude and effectively adapt the quality such as to ensure the highest possible playback quality of the video being viewed. By improving the channel utilization we also address the discrimination problem that HAS clients often suffer from, allowing us to in some cases simultaneously improve the playback quality of the video being viewed and provide the value-added service of allowing instantaneous playback of the prefetched alternative videos.

Software

After the summer vacation, the software and code used in our paper will be made available here (13 MB) for use by the wider research community. Please refer to our paper above for a description of the different components and the experimental setup. (The file contains commented source codes and a README file which should help in getting started with the files.)

Note: If you use our datafiles and/or software in your research, please include a reference to our ACM MM 2015 paper (pdf) in your work.