Supervised-learning-Based QoE Prediction of Video Streaming in Future Networks: A Tutorial with Comparative Study

Quality of experience (QoE)-based service management remains key for successful provisioning of multimedia services in next-generation networks such as 5G/6G, which requires proper tools for quality monitoring, prediction, and resource management where machine learning (ML) can play a crucial role. In this article, we provide a tutorial on the development and deployment of the QoE measurement and prediction solutions for video streaming services based on supervised learning ML models. First, we provide a detailed pipeline for developing and deploying super-vised-learning-based video streaming QoE prediction models that covers several stages including data collection, feature engineering, model optimization and training, testing and prediction, and evaluation. Second, we discuss the deployment of the ML model for QoE prediction/measurement in 5G/6G networks using network-enabling technologies such as software-defined networking, network function virtualization, and multi-access edge computing by proposing reference architecture. Third, we present a comparative study of the state-of-the-art supervised learning ML models for QoE prediction of video streaming applications based on multiple performance metrics.