Benchmarking Machine Learning Models for QoE Estimation in Video Streaming: Accuracy, Efficiency, Confidence and Explainability

The accurate prediction of Quality of Experience (QoE) in video streaming services is essential for optimizing user satisfaction and network performance. While traditional Quality of Service (QoS) metrics provide objective measurements of network behavior, they often fail to reflect the subjective nature of user experience. This paper investigates the use of Machine Learning models to estimate QoE based on QoS indicators. Building upon the recently published SNESet dataset, we evaluate a range of modern regression techniques, including randomization-based neural networks, symbolic regression and Kolmogorov-Arnold Networks, alongside other traditional and ensemble-based models. A central focus of this study is the explainability of such new models, which enables the extraction of domain-relevant insights from the learned relationships. Using model-agnostic techniques for explainable Artificial Intelligence and uncertainty quantification, we assess the confidence of such models in their predictions and analyze the contribution of individual features to the estimated QoE. Our results underscore the need for explainable QoE prediction systems, closing the gap between data-driven modeling and domain expertise.