On-Line Learning and Optimization for Wireless Video Transmission

In this paper, we address the problem of how to optimize the cross-layer transmission policy for delay-sensitive video streaming over slow-varying flat-fading wireless channels on-line, at transmission time, when the environment dynamics are unknown. We first formulate the cross-layer optimization u...

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Veröffentlicht in:IEEE transactions on signal processing Jg. 58; H. 6; S. 3108 - 3124
Hauptverfasser: Yu Zhang, Fangwen Fu, van der Schaar, Mihaela
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.06.2010
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Access protocols
Algorithms
Applied sciences
Asymptotic properties
Channels
Computational complexity
Delay effects
Dynamic programming
Dynamics
Exact sciences and technology
Exchange
Heuristic algorithms
Information, signal and communications theory
Layered Markov decision process
layered real-time dynamic programming
Mathematical models
Miscellaneous
on-line learning
On-line systems
Open systems
Optimization
Optimization methods
Policies
Signal processing
Streaming media
Studies
Telecommunication traffic
Telecommunications and information theory
Wireless networks
wireless video transmission
Performance evaluation
Markov process
Markov decision
Wireless telecommunication
Video signal
Optimization
Learning
Cross-layer approach
Signal transmission
Dynamic programming
on-line learning
Layered Markov decision process
Fading channels
Streaming
wireless video transmission
Algorithm
Computational complexity
Information transmission
Transmission time
layered real-time dynamic programming
Optimal solution
Telecommunication channels
A priori estimation
Signal processing
OSI model
ISSN:1053-587X, 1941-0476
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Beschreibung
Zusammenfassung:In this paper, we address the problem of how to optimize the cross-layer transmission policy for delay-sensitive video streaming over slow-varying flat-fading wireless channels on-line, at transmission time, when the environment dynamics are unknown. We first formulate the cross-layer optimization using a systematic layered Markov decision process (MDP) framework, which complies with the layered architecture of the OSI stack. Subsequently, considering the unknown dynamics of the video sources and underlying wireless channels, we propose a layered real-time dynamic programming (LRTDP) algorithm, which requires no a priori knowledge about the source and network dynamics. LRTDP allows each layer to learn the dynamics on-the-fly, and adjusts its policy autonomously, based on their experienced dynamics as well as limited message exchanges with other layers. Unlike existing cross-layer methods, LRTDP optimizes the cross-layer policy in a layered and on-line fashion, exhibits a low computational complexity, requires limited message exchanges among layers, and is capable to adapt on-the-fly to the experienced environment dynamics. Finally, we prove that LRTDP converges to the optimal cross-layer policy asymptotically. Our numerical experiments show that LRTDP provides comparable performance to the idealized optimal cross-layer solutions based on complete knowledge.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2010.2046040