# Generative QoE Modeling: A Lightweight Approach for Telecom Networks

arXiv: https://arxiv.org/abs/2504.21353 · HTML: https://mlsanity.com/paper/2504.21353/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Learning to Predict Streaming Video QoE: Distortions, Rebuffering and Memory](https://mlsanity.com/paper/1703.00633/) (2017)
- [Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach](https://mlsanity.com/paper/1807.07126/) (2018)
- [Sector-Based Radio Resource Allocation (SBRRA) Algorithm for Better Quality of Service and Experience in Device-to-Device (D2D) Communication](https://mlsanity.com/paper/1801.06866/) (2018)

## Abstract (arXiv)

Quality of Experience (QoE) prediction plays a crucial role in optimizing resource management and enhancing user satisfaction across both telecommunication and OTT services. While recent advances predominantly rely on deep learning models, this study introduces a lightweight generative modeling framework that balances computational efficiency, interpretability, and predictive accuracy. By validating the use of Vector Quantization (VQ) as a preprocessing technique, continuous network features are effectively transformed into discrete categorical symbols, enabling integration with a Hidden Markov Model (HMM) for temporal sequence modeling. This VQ-HMM pipeline enhances the model's capacity to capture dynamic QoE patterns while supporting probabilistic inference on new and unseen data. Experimental results on publicly available time-series datasets incorporating both objective indicators and subjective QoE scores demonstrate the viability of this approach in real-time and resource-constrained environments, where inference latency is also critical. The framework offers a scalable alternative to complex deep learning methods, particularly in scenarios with limited computational resources or where latency constraints are critical.