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
The software and code used in our paper is 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.