YouTube goes 5G: QoE Benchmarking and ML-based Stall Prediction - Université Côte d'Azur Access content directly
Conference Papers Year : 2024

YouTube goes 5G: QoE Benchmarking and ML-based Stall Prediction

Abstract

Given the dominance of adaptive video streaming services on the Internet traffic, understanding how YouTube Quality of Experience (QoE) relates to real 4G and 5G Channel Level Metrics (CLM) is of interest to not only the research community but also to Mobile Network Operators (MNOs) and content creators. In this context, we collect YouTube and CLM logs with 1-second granularity spanning a six-month period. We group the traces by their context, i.e., Mobility, Pedestrian, Bus/Railway terminals, and Static Outdoor, and derive key performance footprints of real 4G and 5G video streaming in the wild. We also develop Machine Learning (ML) classifiers to predict objective QoE video stalls by using past patterns from CLM traces. We release all datasets and software artifacts for reproducibility purposes.
Fichier principal
Vignette du fichier
WCNC2024.pdf (664.54 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04400816 , version 1 (17-01-2024)

Identifiers

  • HAL Id : hal-04400816 , version 1

Cite

Raza Ul Mustafa, Chadi Barakat, Christian Esteve Rothenberg. YouTube goes 5G: QoE Benchmarking and ML-based Stall Prediction. IEEE Wireless Communications and Networking Conference (WCNC), Apr 2024, Dubai, United Arab Emirates. ⟨hal-04400816⟩
229 View
123 Download

Share

Gmail Mastodon Facebook X LinkedIn More