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Bargaining Strategies for Networked Multimedia Resource Management

Hyunggon Park   van der Schaar, M.  
California Univ., Los Angeles
This paper appears in: Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on]
Publication Date: July 2007
Volume: 55 , Issue: 7 , Part 1
On page(s): 3496 - 3511
ISSN: 1053-587X
Digital Object Identifier: 10.1109/TSP.2007.893755
Current Version Published: 2007-06-18

Abstract
Multiuser multimedia applications such as enterprise streaming, surveillance, and gaming are recently emerging, and they are often deployed over bandwidth-constrained network infrastructures. To ensure the quality of service (QoS) required by the delay-sensitive and bandwidth intensive multimedia data for these applications, efficient resource (bandwidth) management becomes paramount. We propose to deploy the well-known game theoretic concept of bargaining to allocate the bandwidth fairly and optimally among multiple collaborative users. Specifically, we consider two bargaining solutions for our resource management problem: the Nash bargaining solution (NBS) and the Kalai-Smorodinsky bargaining solution (KSBS). We provide interpretations for the two investigated bargaining solutions for multiuser resource allocation: the NBS can be used to maximize the system utility, while the KSBS ensures that all users incur the same utility penalty relative to the maximum achievable utility. The bargaining strategies and solutions are implemented in the network using a resource manager, which explicitly considers the application-specific distortion for the bandwidth allocation. We show that the bargaining solutions exhibit important properties (axioms) that can be used for effective multimedia resource allocation. Moreover, we propose several criteria for determining bargaining powers for these solutions, which enable us to provide additional flexibility in choosing solution by taking into consideration the visual quality impact, the deployed spatiotemporal resolutions, etc. We also determine the complexity of these solutions for our application and quantify the performance of the proposed bargaining-based resource strategies for different scenarios.

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