Optimal Layered Video IPTV Multicast Streaming Over Mobile WiMAX Systems

A WiMAX radio resource allocation (WRA) problem is investigated in the context of IPTV broadcasting over mobile WiMAX multicast, broadcast services (MBS) channels. The goal is to maximize quality of services, in terms of number of subscribers served, number of IPTV channels carried, and perceived vi...

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Bibliographic Details
Published inIEEE transactions on multimedia Vol. 13; no. 6; pp. 1395 - 1403
Main Authors Wu, Po-Han, Hu, Yu Hen
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.12.2011
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithmics. Computability. Computer arithmetics
Applied sciences
Broadcasting
Channels
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Exact sciences and technology
Heuristic methods
IEEE 802.16 Standards
IPTV
Mobile communication
Multicast
Multimedia
Optimization
Pareto optimality
Quality of service
Resource allocation
Software
Streaming media
SVC
Theoretical computing
utility-based
WiMAX
Streaming
utility-based
SVC
Channel capacity
Social perception
Pareto optimum
Internet protocol
Resource allocation
WiMAX
Distributed system
IPTV
Time variation
Polynomial time
Multicast
Image quality
Polynomial complexity
Television
Heuristic method
Wireless network
Image transmission
Time complexity
Service quality
Mobile computing
High rate transmission
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Summary:A WiMAX radio resource allocation (WRA) problem is investigated in the context of IPTV broadcasting over mobile WiMAX multicast, broadcast services (MBS) channels. The goal is to maximize quality of services, in terms of number of subscribers served, number of IPTV channels carried, and perceived video qualities of individual viewers, subject to constraints on multicast channel capacities and space-time channel quality variations. We present an efficient heuristic algorithm based on the Pareto principle that achieves near-optimal results in polynomial time complexity. Extensive simulations are conducted to compare this new approach against state-of-the-art heuristic algorithms and consistent superior performance has been observed.
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ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2011.2168196