Deep learning-based QoE prediction for streaming services in mobile networks

Official URL: https://dx.doi.org/10.1109/WiMob55322.2022.9941672

Video streaming accounts for the most of the global Internet traffic and providing a high user Quality of Experience (QoE) is considered an essential target for mobile network operators (MNOs). QoE strongly depends on network Quality of Service (QoS) parameters. In this work, we use real-world network traces obtained from a major cellular operator in Turkey to establish a mapping from network side parameters to the user QoE. To this end, we use a model-aided deep learning method for first predicting channel path loss, and then, employ this prediction for predicting video streaming MOS. The experimental results demonstrate that the proposed model-aided deep learning model can guarantee higher prediction accuracy compared to predictions only relying on mathematical models. We also demonstrate that even though a trained model cannot be directly transferred from one geographical area to another, they significantly reduce the volume of required training when used for prediction in a new area.