Efficient Video QoE Prediction in Intelligent O-RAN

Abstract

Open Radio Access Network (O-RAN) is a platform developed by a collaboration between wireless operators, infrastructure vendors, and service providers for deploying mobile fronthaul and midhaul networks, built entirely on cloud-native principles. The vision of O-RAN lies in the virtualization of traditional wireless infrastructure components, like Central Units (CU), Radio Units (RU), and Distributed Units (DU). O-RAN decouples the above-mentioned wireless infrastructure components into open-source elements, operating consistently with other elements of different vendors in the network. Quality of Experience (QoE) deals with a user's subjective measure of satisfaction. RAN Intelligent Controller (RIC) in O-RAN provides flexibility to intelligently program and control RAN functions using AI/ML-based models. We argue that various QoE parameters can be measured and operated by the RIC in O-RAN. We propose to improve the efficiency of O-RAN's radio resources by creating a RIC xApp that estimates the QoE measured using Video Mean Opinion of Score (MOS), and accurately optimizes the usage of radio resources across multiple network slices. We use predictive AI/ML-based models to accurately predict the QoE parameters in the network after which we can optimize the usage of network compo-nents leading to an enhanced user experience. Simulation results on 3 simulated data sets show that our proposed approach can achieve up to 95% QoE prediction accuracy.