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

Table 5. QoEestimation model

Model	RMSE	R² Score	Computation Complexity	Observations
Exponential Mapping	0.48	0.81	Low	Captures rapid QoE decline at early QoS degradation but underestimates recovery at low loss.
Logistic Model	0.42	0.84	Low	Models saturation behavior accurately but less adaptable across scenarios.
Random Forest Regression	0.25	0.92	Medium	Provides robust prediction but needs large training data.
Neural Network Model	0.22	0.95	High	Best prediction accuracy; effectively models nonlinearities.
Proposed Hybrid Model	0.19	0.97	Moderate	Achieves optimal trade-off between accuracy and complexity.