ML-Powered KQI Estimation for XR Services: A Case Study on 360-Video

The emergence of cutting-edge technologies and services such as Extended Reality (XR) promises to change how people approach everyday living. At the same time, the emergence of modern and decentralized architectural approaches has ushered in a new generation of mobile networks, such as 5G, as well as outlining the roadmap for B5G (Beyond-5G) and further advancements. These networks are expected to be the enablers for the realization of the metaverse and other futuristic services. In this context, quantifying the service performance is a key enabler for dynamic, environment-adaptive, and proactive network management. This work presents an ML-based (Machine Learning) framework that uses data from the network, such as radio measurements, statistics, and configuration parameters to infer the best ML models that fit diverse XR Key Quality Indicators (KQIs). The output models integrate feature engineering techniques that enhance model size and performance. The proposed framework comprises data preprocessing, model definition, training, tuning, and validation. Additionally, to select the best combination algorithm this work introduces a metric called PET_{score}, which evaluates algorithm candidates in terms of error performance and prediction time. These are considerations that are needed for time-sensitive services like XR’s. To validate our proposal, the 360-video service has been chosen to demonstrate the potential of this ML framework with a real XR use case. In addition, the dataset generated for the use case evaluation is publicly accessible and properly referenced. Furthermore, this work serves as a foundation for future research on end-to-end (E2E) quality of experience (QoE)-based network management in conjunction with other enabling technologies, including network slicing, virtualization, and multi-access edge computing (MEC).