Dynamic dual-reinforcement-learning routing strategies for quality of experience-aware wireless mesh networking

The impact of transmission impairments such as loss and latency on user perceived quality (QoE) depends on the service type. In a real network, multiple service types such as audio, video, and data coexist. This makes resource management inherently complex and difficult to orchestrate. In this paper, we propose an autonomous Quality of Experience management approach for multiservice wireless mesh networks, where individual mesh nodes apply reinforcement learning methods to dynamically adjust their routing strategies in order to maximize the user perceived QoE for each flow. Within the forwarding nodes, we develop a novel packet dropping strategy that takes into account the impact on QoE. Finally, a novel source rate adaptation mechanism is designed that takes into account the expected QoE in order to match the sending rate with the available network capacity. An evaluation of our mechanisms using simulations demonstrates that our approach is superior to the standard approaches, AODV and OLSR, and effectively balances the user perceived QoE between the service flows.