Quality of Experience (QoE) and Network Performance Modelling for Multimedia Traffic

Table 2. Comparison of QoE Assessment Methods

Method Type	Technique / Example	Measurement Basis	Advantages	Limitations	Typical Use Case
Subjective	Mean Opinion Score (MOS), ITU-T P.800, DSCQS	Human perception and user ratings	High accuracy, directly reflects user perception	Costly, time-consuming, not scalable	Laboratory testing, service validation
Objective (Signal-Based)	PSNR, SSIM, VMAF	Comparison of original and transmitted signals	Automated, reproducible, quick analysis	Ignore human perception nuances	Video streaming quality benchmarking
Objective (Parametric / Hybrid)	ITU-T P.1203, E-model	Uses network and codec parameters to infer QoE	Real-time estimation, scalable	Requires calibration, may vary by scenario	Network performance monitoring, adaptive streaming
Data-Driven (Machine Learning)	Random Forest, SVM, Deep Neural Networks	Predicts QoE from QoS datasets	Adaptive, captures nonlinear relations	Needs large training data, model interpretability issues	Intelligent QoE prediction, self-optimizing networks