We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Peer-Assisted On-Demand Streaming: Characterizing Demands and Optimizing Supplies

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Fangming Liu ; Services Comput. Technol. & Syst. Lab., Huazhong Univ. of Sci. & Technol., Wuhan, China ; Bo Li ; Baochun Li ; Hai Jin

Nowadays, there has been significant deployment of peer-assisted on-demand streaming services over the Internet. Two of the most unique and salient features in a peer-assisted on-demand streaming system are the differentiation in the demand (or request) and the prefetching capability with caching. In this paper, we develop a theoretical framework based on queuing models, in order to 1) justify the superiority of service prioritization based on a taxonomy of requests, and 2) understand the fundamental principles behind optimal prefetching and caching designs in peer-assisted on-demand streaming systems. The focus is to instruct how limited uploading bandwidth resources and peer caching capacities can be utilized most efficiently to achieve better system performance. To achieve these objectives, we first use priority queuing analysis to prove how service quality and user experience can be statistically guaranteed, by prioritizing requests in the order of significance, including urgent playback (e.g., random seeks or initial startup), normal playback, and prefetching. We then proceed to construct a fine-grained stochastic supply-demand model to investigate peer caching and prefetching as a global optimization problem. This not only provides insights in understanding the fundamental characterization of demand, but also offers guidelines toward optimal prefetching and caching strategies in peer-assisted on-demand streaming systems.

Published in:

Computers, IEEE Transactions on  (Volume:62 ,  Issue: 2 )