Hybrid EXGBStackQoE Classifier and Stackelberg Game-Theoretic Approaches for Enhanced QoE in Video Services Over 5G Wireless Networks
Video streaming applications have experienced significant growth in recent years, driving an increase in global internet traffic. This rapid expansion underscores the critical need to ensure a high Quality of Experience (QoE) for users, as subpar video QoE can result in considerable financial losses...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10971361/ |
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| Summary: | Video streaming applications have experienced significant growth in recent years, driving an increase in global internet traffic. This rapid expansion underscores the critical need to ensure a high Quality of Experience (QoE) for users, as subpar video QoE can result in considerable financial losses for telecommunication providers. Traditional approaches, such as the Mean Opinion Score (MOS), while widely used for assessing QoE, are inherently subjective and require significant manual effort. This research addresses these limitations by introducing an advanced framework integrating multiple models and methodologies to enhance video QoE in 5G networks. The proposed framework features a novel Hybrid EnhancedXGBStackQoE (EXGBStackQoE) analytical model that applies a two-level stacking technique combined with 5-fold cross-validation to mitigate overfitting. At the initial level, various machine learning (ML) models are trained using the entire dataset, while the subsequent level leverages meta-features generated from the initial predictions to improve overall accuracy. This hybrid model demonstrates an accuracy improvement of 5–7% compared to traditional models, establishing a new standard in QoE evaluation. Additionally, the framework incorporates a Stackelberg Game-based Software-Defined Networking (SbSG) model designed for efficient data offloading in 5G Macro Base Stations (MBS). This model employs economic incentives and traffic load balancing to strategically select users for offloading, prioritizing those with lower QoS based on the Received Signal Strength Indicator (RSSI). By optimizing data distribution between MBS and Heterogeneous Networks (HetNets), the SbSG model enhances both network throughput and service quality. This comprehensive framework addresses critical challenges in video QoE optimization, providing a robust and automated solution tailored to the demands of next-generation networks. |
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| ISSN: | 2169-3536 |