ML-powered KQI estimation for XR services. A case study on 360-Video. (arXiv:2212.12002v1 [cs.NI])

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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