Classification-Based System For Cross-Layer Optimized Wireless Video Transmission

• Published in: IEEE transactions on multimedia Vol. 8; no. 5; pp. 1082 - 1095
• Main Authors: van der Schaar, M., Turaga, D.S., Raymond Wong
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-10-2006; Institute of Electrical and Electronic Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access methods and protocols, osi model; Applied sciences; Broadcasting. Videocommunications. Audiovisual; Channels; Classification; Classification for delay-constrained video transmission; Classifiers; cross-layer optimization; Data mining; Exact sciences and technology; Machine learning; Mathematical models; Multimedia systems; Optimization; Runtime; Strategy; Studies; Telecommunications; Telecommunications and information theory; Telecommunications industry; Teleprocessing networks. Isdn; Training; Training data; Video compression; Video sequences; Video transmission; Wireless application protocol; Wireless LAN; Wireless networks; Wireless LAN; Image sequence; Videocommunication; Loss rate; cross-layer optimization; Wireless network; Distributed system; Transmission loss; Transmission time; Optimization; Classification for delay-constrained video transmission
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2006.879827

Joint optimization strategies across various layers of the protocol stack have recently been proposed for improving the performance of real-time video transmission over wireless networks. In this paper, we propose a new, low complexity system for determining the optimal cross-layer strategies for wireless multimedia transmission based on classification and machine learning techniques. We first determine offline the optimal cross-layer strategy for various video sequences and channel conditions (training data). Subsequently, we extract relevant and easy to compute content features, encoder-specific parameters, and channel resources from the training data, and train a statistical classifier based on these optimal results. At run-time, we predict using the classifier the optimal cross-layer compression and transmission strategy using these simple, on-the-fly computed features. Hence, we consider the complex problem of finding the optimal cross-layer strategy during the training phase only, and rely at transmission-time on low-complexity classification techniques. We illustrate the proposed classification-based system by performing MAC-application layer optimizations for video transmission over 802.11a wireless LANs. Specifically, we predict the optimal MAC retry limits for the various video packets and compare our results against both optimal and conventionally used ad-hoc cross-layer solutions. Our results indicate that considerable improvements can be obtained through the proposed cross-layer techniques relying on classification as opposed to optimized ad-hoc solutions. The improvements are especially important at high packet-loss rates (5% and higher), where deploying a judicious mixture of strategies at the various layers becomes essential. Furthermore, our proposed classification-based system can be easily modified to include other layers from the OSI stack during the cross-layer optimization