Enterprise AI Analysis
Personalized QoE Prediction: A Demographic-Augmented Machine Learning Framework for 5G Video Streaming Networks
This paper introduces a novel demographic-aware Quality of Experience (QoE) prediction framework for 5G video streaming networks. It leverages data augmentation with synthetic demographic profiles and advanced deep learning models like TabNet to improve prediction accuracy and robustness, accounting for user perception diversity. Experimental results demonstrate significant performance gains over traditional methods.
Authors: Maryam Khalid, Zunaira Ahmed, Hijab Beg, Mohsin Khan
Executive Impact & Key Findings
Our analysis reveals the most significant implications and breakthroughs for enterprise adoption of AI in QoE prediction.
6x
Dataset Expanded
40%
RMSE Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Demographic-Aware Data Augmentation
The framework introduces a novel demographic-aware data augmentation strategy. It constructs behaviorally realistic synthetic demographic profiles to model diverse user sensitivities to QoE factors like stalling, bitrate variations, and visual degradation. The original QoE dataset is expanded sixfold (from 450 to 2700 samples), creating a richer and more diverse dataset that reflects real differences in user perception. This addresses the limitation of previous studies that relied on uniform user assumptions and limited datasets, thereby improving the robustness of QoE prediction.
Advanced Deep Learning Models
The research evaluates a comprehensive set of classical ML models and state-of-the-art deep neural architectures, including attention-based networks and TabNet. TabNet, an attentive, feature-selection-driven deep learning model, achieves the strongest performance. It demonstrates superior interpretability and generalization on augmented QoE features due to its ability to learn complex feature dependencies and perform automatic feature selection at multiple sequential steps using sparse attention masks. This enables the model to focus on the most relevant QoE-related signals for each decision step.
Personalized QoE Prediction
A key contribution is the demographic-driven Mean Opinion Score (MOS) adjustment function. This function simulates realistic variations in QoE perception across different user groups based on six behaviorally realistic demographic profiles (e.g., casual viewer, quality enthusiast, mobile user, gamer/sports viewer, elderly user, professional-critical user). By incorporating these profiles, the model learns to assign different MOS values for identical video session features depending on the user's demographic profile, making QoE predictions more reflective of real-world user diversity and enabling personalized service delivery in 5G/6G networks.
Real-World Impact and Future Directions
The proposed framework significantly enhances QoE prediction accuracy and robustness, offering a more realistic and scalable direction for future QoE-aware network intelligence. Experimental results show significant performance gains across RMSE, MAE, and R² metrics compared to baseline models. The approach is particularly relevant for 5G/6G network slicing scenarios, where dynamic resource allocation based on individual user sensitivities can optimize service delivery. Future work involves testing with actual real-user data and refining user personas based on more detailed studies.
Demographic-Aware QoE Prediction Pipeline
Data Layer (Original QoE + Demographic Profiles)
→
Processing Layer (Feature Preprocessing)
→
Modeling Layer (Classical ML + Deep Learning)
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Evaluation Layer (Metrics)
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Output Layer (Comparison & Reporting)
| Feature | Traditional ML | Demographic-Augmented DL |
|---|---|---|
| Dataset Size | Small, uniform (450 samples) | Expanded, diverse (2700 samples) |
| User Diversity | Not accounted for | Six behaviorally realistic profiles |
| Model Focus | Limited datasets, simple ML | Complex dependencies, advanced DL (TabNet, AttentionMLP) |
| Generalization | Poor in diverse scenarios | Enhanced, robust across user groups |
Real-world Scenario: 5G Network Slicing for Personalized QoE
In a typical urban 5G environment, the framework dynamically adjusts resources for different user types (e.g., gamers, commuters, elderly) to optimize their individual QoE, preventing service degradation for sensitive users while efficiently allocating bandwidth. This personalized approach ensures optimal service delivery for diverse user demands within the same network slice.
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Estimated Annual Savings
$0
Employee Hours Reclaimed Annually
0
Your Implementation Roadmap
A typical rollout of an AI-powered QoE prediction system involves these key phases.
Phase 1: Discovery & Strategy
Initial consultation to define objectives, assess current infrastructure, and tailor a strategic plan for integrating demographic-aware QoE prediction into your 5G/6G networks. Data collection strategy for demographic profiles.
Phase 2: Data Engineering & Model Customization
Setting up data pipelines, implementing demographic-aware data augmentation, and customizing deep learning models (e.g., TabNet, AttentionMLP) for your specific network characteristics and user base.
Phase 3: Integration & Testing
Seamless integration of the prediction framework with existing network management systems. Comprehensive testing in simulated and real-world 5G environments to validate accuracy and performance.
Phase 4: Deployment & Optimization
Full deployment of the personalized QoE prediction engine, followed by continuous monitoring, fine-tuning, and iterative optimization to ensure sustained high performance and user satisfaction.
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