Supervised Learning based QoE Prediction of Video Streaming in Future Networks: A Tutorial with Comparative Study

TL;DR

This paper provides a comprehensive tutorial on supervised learning methods for predicting video streaming QoE in future networks, including development pipelines, deployment strategies, and a comparative analysis of models.

Contribution

It offers a detailed development and deployment framework for supervised learning-based QoE prediction models in 5G/6G networks, along with a comparative study of existing models.

Findings

Proposed a reference architecture for deploying ML-based QoE prediction in next-gen networks.

Presented a detailed pipeline covering data collection to evaluation for QoE models.

Compared state-of-the-art supervised learning models based on multiple performance metrics.

Abstract

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.