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Author:

Si, Pengbo (Si, Pengbo.) | Yu, F. Richard (Yu, F. Richard.) | Zhang, Yanhua (Zhang, Yanhua.) (Scholars:张延华)

Indexed by:

EI Scopus

Abstract:

In mobile cloud computing (MCC) systems, the resource in both the cloud and the mobile network should be carefully managed. Cloud resource management and radio resource management have traditionally been addressed separately in previous works. In this paper, we propose to jointly study dynamic cloud and radio resource management so as to improve end-to-end performance of adaptive video transmissions in MCC systems. Video application quality of service performance, distortion, is adopted as the performance measure. An important video application layer parameter, intra-refreshing rate, is optimized to improve the video distortion performance. We formulate the problem as a stochastic restless bandits optimization problem, which facilitates the distributed MCC architecture and simplifies the computation and implementation due to its 'indexibility' property. Simulation results are presented to show the effectivenes of the proposed scheme. © 2014 IEEE.

Keyword:

Natural resources management Image communication systems Mobile cloud computing Resource allocation Quality of service Stochastic systems Radio communication Radio transmission

Author Community:

  • [ 1 ] [Si, Pengbo]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
  • [ 2 ] [Yu, F. Richard]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
  • [ 3 ] [Yu, F. Richard]Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
  • [ 4 ] [Zhang, Yanhua]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China

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Source :

Year: 2014

Page: 2270-2275

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 8

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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