Network QoE metrics for assessing system-level performance of radio resource management algorithms in LTE networks

In this paper, the quality of experience (QoE) of real-time video streaming over long term evolution (LTE) networks is investigated. Network QoE metrics are proposed in order to capture the overall performance of radio resource management algorithms in terms of video quality perceived by the end users. Thus, real time video streaming over both the uplink (UL) and downlink (DL) directions is investigated, and LTE dynamic resource allocation is taken into account. Metrics corresponding to average, geometric mean, and minimum QoE in the network are measured when max C/I, proportional fair, and max-min radio resource management algorithms are implemented. Monte Carlo simulation results show that proportional fair scheduling maximizes the average network QoE both in the uplink and downlink, and that it also leads to more fairness.