ML-powered KQI estimation for XR services. A case study on 360-Video

• URL: http://arxiv.org/abs/2212.12002v1
• Date: Thu, 8 Dec 2022 17:30:23 GMT
• Title: ML-powered KQI estimation for XR services. A case study on 360-Video
• Authors: O. S. Pe\~naherrera-Pulla and Carlos Baena and Sergio Fortes and Raquel Barco
• Abstract summary: This work presents an ML-based framework that allows the estimation of service Key Quality Indicators (KQIs) For this, only information reachable to operators is required, such as statistics and configuration parameters from these networks. This work will help as a baseline for E2E-Quality-of-Experience-based network management working in conjunction with network slicing, virtualization, and MEC.
• Score: 0.34410212782758043
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The arise of cutting-edge technologies and services such as XR promise to change the concepts of how day-to-day things are done. At the same time, the appearance of modern and decentralized architectures approaches has given birth to a new generation of mobile networks such as 5G, as well as outlining the roadmap for B5G and posterior. These networks are expected to be the enablers for bringing to life the Metaverse and other futuristic approaches. In this sense, this work presents an ML-based (Machine Learning) framework that allows the estimation of service Key Quality Indicators (KQIs). For this, only information reachable to operators is required, such as statistics and configuration parameters from these networks. This strategy prevents operators from avoiding intrusion into the user data and guaranteeing privacy. To test this proposal, 360-Video has been selected as a use case of Virtual Reality (VR), from which specific KQIs are estimated such as video resolution, frame rate, initial startup time, throughput, and latency, among others. To select the best model for each KQI, a search grid with a cross-validation strategy has been used to determine the best hyperparameter tuning. To boost the creation of each KQI model, feature engineering techniques together with cross-validation strategies have been used. The performance is assessed using MAE (Mean Average Error) and the prediction time. The outcomes point out that KNR (K-Near Neighbors) and RF (Random Forest) are the best algorithms in combination with Feature Selection techniques. Likewise, this work will help as a baseline for E2E-Quality-of-Experience-based network management working in conjunction with network slicing, virtualization, and MEC, among other enabler technologies.

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