Quality-adaptive Prefetching for Interactive Branched Video using HTTP-based Adaptive Streaming
Vengatanathan Krishnamoorthi, Niklas Carlsson, Derek Eager, Anirban Mahanti, and Nahid Shahmehri
Paper:
Vengatanathan Krishnamoorthi, Niklas Carlsson, Derek Eager, Anirban Mahanti, and Nahid Shahmehri
"Quality-adaptive Prefetching for Interactive Branched Video using HTTP-based Adaptive Streaming",
Proc. ACM International Conference on Multimedia (ACM MM),
Orlando, FL, Nov. 2014.
(pdf)
Abstract:
Interactive branched video that allows users to select their own paths through the video,
provides creative content designers with great personalization opportunities; however,
such video also introduces significant new challenges for the system developer.
For example, without careful prefetching and buffer management, the use of multiple
alternative playback paths can easily result in playback interruptions. In this paper,
we present a full implementation of an interactive branched video player using HTTP-based
Adaptive Streaming (HAS) that provides seamless playback even when the users defer their
branch path choices to the last possible moment. Our design includes optimized prefetching
policies that we derive under a simple optimization framework, effective buffer management
of prefetched data, and the use of parallel TCP connections to achieve efficient buffer workahead.
Through performance evaluation under a wide range of scenarios, we show that our optimized policies
can effectively prefetch data of carefully selected qualities along multiple alternative paths such
as to ensure seamless playback, offering users a pleasant viewing experience without playback interruptions.
Software
The software and code used in our paper is made available
here (8.5 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 2014 paper
(pdf) in your work.