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 (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 for use by the wider research community. Please refer to our paper above for a description of the different components and the experimental setup. (Additional documentation is availible and/or will be added to the code library during July 2014.)

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.