Abstract:
We develop an optimization framework for Quality of Experience (QoE)-based routing in multi-service Wireless Mesh Networks (WMNs). The framework takes into account the heterogeneous requirements of different services delivered over a WMN, such that the overall end-user QoE is maximized under given resource constraints. We propose a novel QoE aware double reinforcement learning strategy for dynamically computing the most efficient routes to deliver the flows of each service type. Comprehensive NS-2-based simulations demonstrate the substantial performance gains that our approach enables over conventional routing techniques such as AODV, with significant improvement over video quality.
Authors: Ricardo Matos, Nuno Coutinho, Carlos Marques, Susana Sargento, Jacob Chakareskiy and Andreas Kassler