  The 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 paper, 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. Firstly, we provide a detailed pipeline for
developing and deploying supervised learning-based video streaming QoE
prediction models which covers several stages including data collection,
feature engineering, model optimization and training, testing and prediction
and evaluation. Secondly, we discuss the deployment of the ML model for the QoE
prediction/measurement in the next generation networks (5G/6G) using network
enabling technologies such as Software-Defined Networking (SDN), Network
Function Virtualization (NFV) and Mobile Edge Computing (MEC) by proposing
reference architecture. Thirdly, 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.
