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Assessing the quality of experience in wireless networks for multimedia applications: A comprehensive analysis
1Information Engineering School Jiaozuo Normal College, Jiaozuo, 454000, China.
Heliyon
|May 10, 2024
Summary
This study introduces a novel deep learning model for predicting multimedia Quality of Experience (QoE) in wireless networks. The model integrates video, service quality, user behavior, and facial expressions, outperforming existing methods.
Area of Science:
- Computer Science
- Electrical Engineering
- Human-Computer Interaction
Background:
- Increasing demand for mobile multimedia services necessitates accurate Quality of Experience (QoE) evaluation in wireless networks.
- Existing methods for QoE assessment require enhancement to capture the complexities of user satisfaction.
Purpose of the Study:
- To develop and validate a novel deep learning-based method for predicting multimedia QoE in wireless networks.
- To create a comprehensive QoE prediction model integrating diverse data sources.
Main Methods:
- Video session modeling considering temporal intervals.
- Recurrent Neural Networks (RNNs) for QoE prediction.
- Integration of video, Quality of Service (QoS), user behavior, and facial expression data.
Main Results:
- The proposed QoE model demonstrates superior performance over state-of-the-art methods.
- Validation using the RTVCQoE dataset showed improved accuracy in PLCC, SRCC, and KRCC metrics.
- The model effectively predicts user satisfaction in wireless multimedia services.
Conclusions:
- The developed deep learning approach offers a precise and reliable methodology for multimedia QoE evaluation.
- This research contributes to enhancing user satisfaction in wireless multimedia services.
- The findings encourage further innovation in mobile Internet quality assessment.
