Optimized wireless video transmission using classification

Cross protocol layer optimizations have been recently proposed for improving the performance of real-time video transmission over 802.11 WLANs. However, performing such cross-layer optimizations is difficult since the video data and channel characteristics are time-varying, and analytically deriving the relationships between quality and channel characteristics given delay and power constraints is difficult. Furthermore, these relationships are often non-linear and non-deterministic (only worst or average case values can be determined). Complex Lagrangian or multi-objective optimization problems are thus often faced. In this paper, we propose a novel framework for solving cross MAC-application layer optimization problems. More specifically, we employ classification techniques to find an optimized cross-layer strategy for wireless multimedia transmission. Our solution deploys both content- and channel-related features to select a joint application-MAC strategy from the different strategies available at the various layers. Preliminary results indicate that considerable improvements can be obtained through the proposed cross-layer techniques relying on classification as opposed to 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.