BUCKET-FILLING: AN ASYMPTOTICALLY OPTIMAL VIDEO-ON-DEMAND NETWORK WITH SOURCE CODING

MPhil Thesis Defence


Title: "BUCKET-FILLING: AN ASYMPTOTICALLY OPTIMAL VIDEO-ON-DEMAND NETWORK WITH 
SOURCE CODING"

By

Mr. Zhangyu CHANG


Abstract

There has been growing interest in recent years for content providers to 
provide video-on-demand (VoD) as a cloud service. In such a network, the 
content provider may rent heterogeneous resources (such as streaming and 
storage capacities) from geographically distributed data centers deployed close 
to user pools. These data centers (or proxy servers) collaboratively share 
contents with each other to serve their local users. A critical challenge is 
hence to optimize movie storage and retrieval to minimize the deployment cost 
consisting of streaming, storage, and network transmission between data 
centers.

We propose a novel and effective movie storage and retrieval using linear 
source coding. All the movies are source-encoded once at the repository, by 
taking every q source symbols of movie m to generate n(m) coded symbols. These 
coded symbols are then distributed to the servers in the cloud. Based on a 
general and comprehensive cost model, we optimize n(m) and the number of 
symbols to retrieve from remote servers for a local movie request. The optimal 
solution can be efficiently computed with a linear programming (LP) 
formulation. Our solution is proved to approach asymptotically the global 
minimum cost as q increases. Even when q is low (say, 30), near optimality can 
be achieved. To accommodate large movie pool and system parameter changes, we 
propose algorithms for movie grouping and on-line re-optimization which 
significantly reduce the computational complexity with little compromise on 
optimality. Through extensive simulation, our algorithm is shown to achieve 
remarkably the lowest cost, outperforming traditional and stateof- the-art 
heuristics with a substantially wide margin (of multiple times in many cases).


Date:			Monday, 10 August 2015

Time:			5:00pm - 7:00pm

Venue:			Room 2132A
 			Lift 19

Committee Members:	Prof. Gary Chan (Supervisor)
 			Dr. Jogesh Muppala (Chairperson)
 			Dr. Kai Chen


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