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

Autor(es)

Ul Mustafa, Raza ; Barakat, Chadi ; Rothenberg, Christian Esteve

• Tipo de documento: Artigo Científico
• Fonte: 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024; v. N/A, p. 6-pg., 2024-01-01.

Resumo

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. (AU)

• Processo FAPESP: 21/00199-8 - Redes e serviços inteligentes rumo 2030 (SMARTNESS)
• Beneficiário: Christian Rodolfo Esteve Rothenberg
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia