Optimized Adaptive Streaming of Multi-video Stream Bundles

Niklas Carlsson, Derek Eager, Vengatanathan Krishnamoorthi, and Tatiana Polishchuk


Paper: Niklas Carlsson, Derek Eager, Vengatanathan Krishnamoorthi, and Tatiana Polishchuk, "Optimized Adaptive Streaming of Multi-video Stream Bundles", IEEE Transactions on Multimedia (IEEE TMM), to appear. (pdf)

Abstract: In contrast to traditional video, multi-view video streaming allows viewers to interactively switch among multiple perspectives provided by different cameras. One approach to achieving such a service is to encode the video from all of the cameras into a single stream, but this has the disadvantage that only a portion of the received video data will be used, namely that required for the selected view at each point in time. In this paper we introduce the concept of a “multi-video stream bundle” that consists of multiple parallel video streams that are synchronized in time, each providing the video from a different camera capturing the same event or movie. For delivery we leverage the adaptive features and time-based chunking of HTTP-based Adaptive Streaming (HAS), but now employing adaptation in both content and rate. Users are able to change their viewpoint on-demand and the client player adapts the rate at which data is retrieved from each stream based on the user’s current view, the probabilities of switching to other views, and the user’s current bandwidth conditions. A crucial component of such a system is the prefetching policy. For this we present an optimization model as well as a simpler heuristic that can balance the playback quality and the probability of playback interruptions. After analytically and numerically characterizing the optimal solution, we present a prototype implementation and sample results. Our prefetching and buffer management solution is shown to provide close to seamless playback switching when there is sufficient bandwidth to prefetch the parallel streams.

Software

When accepted the software and code used in our paper will be made available here (14 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 IEEE Transactions on Multimedia (IEEE TMM) 2017 paper (pdf) in your work.