Lifetime and Distortion Optimization With Joint Source/Channel Rate Adaptation and Network Coding-Based Error Control in Wireless Video Sensor Networks

• Published in: IEEE transactions on vehicular technology 2011-03, Vol.60 (3), p.1182-1194
• Main Authors: Zou, Junni, Xiong, Hongkai, Li, Chenglin, Zhang, Ruifeng, He, Zhihai
• Format: Article
• Language: English
• Subjects: Adaptation; Algorithms; Applied sciences; Channels; Coding, codes; Decomposition; Distortion; Electrical engineering. Electrical power engineering; Electrical machines; Encoding; Error detection; Exact sciences and technology; Image processing; Information, signal and communications theory; Joints; Lifetime; Network coding; Networks; Optimization; Power demand; power-rate-distortion (P-R-D); Regulation and control; resource allocation; Sensors; Services and terminals of telecommunications; Signal and communications theory; Signal processing; Studies; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Telemetry. Remote supervision. Telewarning. Remote control; Wireless communication; Wireless networks; Wireless sensor networks; wireless video sensor networks (WVSNs)
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2011.2111425

In this paper, we study joint performance optimization on network lifetime and video distortion for an energy-constrained wireless video sensor network (WVSN). To seek an appropriate tradeoff between maximum network lifetime and minimum video distortion, a framework for joint source/channel rate adaptation is proposed, where the video encoding rate, link rate, and power consumption are jointly considered, formulating a weighted convex optimization problem. In the context of lossy wireless channels, an efficient error control scheme that couples network coding and multipath routing is explored. Moreover, an integrated power consumption model, including power dissipation on video compression, error control, and data communication, is specifically developed for the video sensor node. By primal decomposition, the original problem is decomposed into a two-level optimization procedure, with the high-level procedure for source adaptation (source rate optimization) and the low-level procedure for channel adaptation (network resource allocation). Finally, a fully decentralized iterative algorithm is developed to resolve the target optimization problem. Extensive simulation results evaluate the convergence performance of the proposed algorithm and demonstrate the best tradeoff performance.