Caching Scheme in Patch Streaming Multimedia-On-Demand System: Design and Implementation
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Date
2011-08
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Addis Ababa University
Abstract
Multimedia streaming is a technique that allows users to play media content as it is being
received without waiting for the entire file to download. It can be live-streaming or OnDemand.
In On-Demand multimedia streaming systems, streaming techniques are
usually combined with proxy caching to obtain better performance. A number of caching
schemes are proposed and some are optimized for a conventional multicast or batch
streaming system. These caching schemes reduce the startup latency of this technique.
However, patch streaming, which has no startup latency inherent to it, requires extra
bandwidth to deliver the media data in patch streams.
This thesis work proposes a caching technique which aims at reducing the bandwidth
cost of patch streaming technique. It uses the neural networks’ reservoir computing (RC)
approach for the popularity prediction in the optimization of media prefix design and
selection. The system is implemented and the performance of the proposed caching
scheme is compared with the popularity and prefix aware interval caching (2PIC)
scheme [prefix part] and patch streaming with no caching using an extensive simulation
on a synthetically generated media server workload. The bandwidth saving, hit ratio and
concurrent number of clients are used to compare the performance in which the proposed
scheme is found to perform better for different caching capacities of the proxy server.
The bandwidth saving of as high as 36% can be obtained from the proposed scheme
while 32% from 2PIC compared to the no caching scheme for proxy caching capacity of
about 16.7% of the total media size on the server. Higher hit ratio is obtained using
proposed scheme than the 2PIC scheme. The number of concurrent clients that can be
served is large when using proposed scheme followed by 2PIC and no caching schemes.
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Keywords
Access Pattern, Batch Streaming, Multimedia, Patch Streaming, Proxy Caching, Reservoir Computing, Synthetic Workload Generator, Popularity Prediction