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Video streaming applications have grown considerably in recent years. As a result, this becomes one of the most significant contributors to global internet traffic. According to recent studies, the telecommunications industry loses millions of dollars due to poor video Quality of Experience (QoE) for users. Among the standard proposals for standardizing the quality of video streaming over internet service providers (ISPs) is the Mean Opinion Score (MOS). However, the accurate finding of QoE by MOS is subjective and laborious, and it varies depending on the user. A fully automated data analytics framework is required to reduce the inter-operator variability characteristic in QoE assessment. This work addresses this concern by suggesting a novel hybrid XGBStackQoE analytical model using a two-level layering technique. Level one combines multiple Machine Learning (ML) models via a layer one Hybrid XGBStackQoE-model. Individual ML models at level one are trained using the entire training data set. The level two Hybrid XGBStackQoE-Model is fitted using the outputs (meta-features) of the layer one ML models. The proposed model outperformed the conventional models, with an accuracy improvement of 4 to 5 percent, which is still higher than the current traditional models. The proposed framework could significantly improve video QoE accuracy.

A Hybrid Machine Learning Approach for Improvised QoE in Video Services over 5G Wireless Networks