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Mathematical Problems in Engineering
Volume 2016, Article ID 9576502, 10 pages
http://dx.doi.org/10.1155/2016/9576502
Research Article

A Context-Aware Adaptive Streaming Media Distribution System in a Heterogeneous Network with Multiple Terminals

1Computer and Network Center, Communication University of China, Beijing 100024, China
2Information Engineering School, Communication University of China, Beijing 100024, China

Received 24 August 2015; Accepted 14 January 2016

Academic Editor: Panos Liatsis

Copyright © 2016 Yepeng Ni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We consider the problem of streaming media transmission in a heterogeneous network from a multisource server to home multiple terminals. In wired network, the transmission performance is limited by network state (e.g., the bandwidth variation, jitter, and packet loss). In wireless network, the multiple user terminals can cause bandwidth competition. Thus, the streaming media distribution in a heterogeneous network becomes a severe challenge which is critical for QoS guarantee. In this paper, we propose a context-aware adaptive streaming media distribution system (CAASS), which implements the context-aware module to perceive the environment parameters and use the strategy analysis (SA) module to deduce the most suitable service level. This approach is able to improve the video quality for guarantying streaming QoS. We formulate the optimization problem of QoS relationship with the environment parameters based on the QoS testing algorithm for IPTV in ITU-T G.1070. We evaluate the performance of the proposed CAASS through 12 types of experimental environments using a prototype system. Experimental results show that CAASS can dynamically adjust the service level according to the environment variation (e.g., network state and terminal performances) and outperforms the existing streaming approaches in adaptive streaming media distribution according to peak signal-to-noise ratio (PSNR).