@inproceedings{214b091f7689405baa958d1e1ba6aa71,
title = "QoE-Analysis of 5G Network Resource Allocation Schemes for Competitive Multi-User Video Streaming Applications",
abstract = "Competitive demand for network resources has only increased during the emergence of 5G next generation cellular technology. As video streaming accounts for an overwhelming percentage of this demand, the importance of considering the often-neglected Quality of Experience (QoE) metric is essential to ensure network resources are allocated in the most effective manner. Generalized network throughput metrics are insufficient in capturing the full human experience as increased data rates do not necessarily translate to improvements in user utility. Our study compares the efficacy of existing network allocation algorithms and proposes new approaches to 5G network resource allocation schemes using a more inclusive snapshot of user demand. We provide recommendations on which approach provides the highest QoE performance and suggestions for future network-side improvements. We further propose a QoE-driven network resource allocation (QENA) algorithm that shows a 20\% improvement in overall average QoE across a large set of heterogeneous users.",
author = "Wheatman, \{Kristina Sorensen\} and Fidan Mehmeti and Mark Mahon and Porta, \{Thomas La\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 97th IEEE Vehicular Technology Conference, VTC 2023-Spring ; Conference date: 20-06-2023 Through 23-06-2023",
year = "2023",
doi = "10.1109/VTC2023-Spring57618.2023.10200845",
language = "English (US)",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings",
address = "United States",
}