Service-aware Maximum Likelihood-based Network Slicing for Live Low-latency Streaming

Zhaozhou Wu; Anderson C Bravalheri; Juan Marcelo Parra Ullauri; Yulei Wu; Dimitra Simeonidou

doi:10.1109/WCNC61545.2025.10978784

Service-aware Maximum Likelihood-based Network Slicing for Live Low-latency Streaming

Zhaozhou Wu, Anderson C Bravalheri, Juan Marcelo Parra Ullauri, Yulei Wu, Dimitra Simeonidou

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

Network slicing (NS) is a promising solution for media services, such as live streaming in telecom networks. NS enables customised network conditions for different applications and requirements. This customisation granularity is further enhanced through the use of 5G Quality of Service (QoS) flows for intra-slice management. Given the fact that network slices are becoming more dedicated to specific services' quality requirements, it becomes important to find service-aware NS methods that guarantee service quality while minimising resource consumption. To this end, this paper presents a Maximum Likelihood-based Network Slicing (MaxLiNS) method that minimises resource consumption with guaranteed Quality of Experience (QoE) for live low-latency streaming service under playback buffer level estimation and control. We evaluated the MaxLiNS method against existing service-agnostic and emerging service-aware NS methods on our End-to-End (E2E) 5G testbed. The results show that the MaxLiNS outperforms existing NS methods in terms of quality guarantee and resource consumption minimisation.

Original language	English
Title of host publication	2025 IEEE Wireless Communications and Networking Conference (WCNC)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	6
ISBN (Electronic)	9798350368369
ISBN (Print)	9798350368376
DOIs	https://doi.org/10.1109/WCNC61545.2025.10978784
Publication status	Published - 9 May 2025
Event	IEEE Wireless Communications and Networking Conference: 6G Horizons: Bridging Beyond Wireless - Mico Milano Congressi, Milan, Italy Duration: 24 Mar 2025 → 27 Mar 2025 https://wcnc2025.ieee-wcnc.org/

Publication series

Name	IEEE Conference on Wireless Communications and Networking
Publisher	IEEE
ISSN (Print)	1525-3511
ISSN (Electronic)	1558-2612

Conference

Conference	IEEE Wireless Communications and Networking Conference
Abbreviated title	IEEE WCNC
Country/Territory	Italy
City	Milan
Period	24/03/25 → 27/03/25
Internet address	https://wcnc2025.ieee-wcnc.org/

Research Groups and Themes

Smart Internet Lab

Keywords

5G/B5G
Network Slicing
Service-aware
Live Low-latency Streaming

Access to Document

10.1109/WCNC61545.2025.10978784

Full-text PDF (accepted manuscript)
This is the accepted author manuscript (AAM) of the article which has been made Open Access under the University of Bristol's Scholarly Works Policy. The final published version (Version of Record) can be found on the publisher's website. The copyright of any third-party content, such as images, remains with the copyright holder.
Accepted author manuscript, 461 KBLicence: CC BY

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Wu, Z., Bravalheri, A. C., Parra Ullauri, J. M., Wu, Y., & Simeonidou, D. (2025). Service-aware Maximum Likelihood-based Network Slicing for Live Low-latency Streaming. In 2025 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE Conference on Wireless Communications and Networking). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/WCNC61545.2025.10978784

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title = "Service-aware Maximum Likelihood-based Network Slicing for Live Low-latency Streaming",

abstract = "Network slicing (NS) is a promising solution for media services, such as live streaming in telecom networks. NS enables customised network conditions for different applications and requirements. This customisation granularity is further enhanced through the use of 5G Quality of Service (QoS) flows for intra-slice management. Given the fact that network slices are becoming more dedicated to specific services' quality requirements, it becomes important to find service-aware NS methods that guarantee service quality while minimising resource consumption. To this end, this paper presents a Maximum Likelihood-based Network Slicing (MaxLiNS) method that minimises resource consumption with guaranteed Quality of Experience (QoE) for live low-latency streaming service under playback buffer level estimation and control. We evaluated the MaxLiNS method against existing service-agnostic and emerging service-aware NS methods on our End-to-End (E2E) 5G testbed. The results show that the MaxLiNS outperforms existing NS methods in terms of quality guarantee and resource consumption minimisation.",

keywords = "5G/B5G, Network Slicing, Service-aware, Live Low-latency Streaming",

author = "Zhaozhou Wu and Bravalheri, {Anderson C} and {Parra Ullauri}, {Juan Marcelo} and Yulei Wu and Dimitra Simeonidou",

year = "2025",

month = may,

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doi = "10.1109/WCNC61545.2025.10978784",

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note = "IEEE Wireless Communications and Networking Conference : 6G Horizons: Bridging Beyond Wireless, IEEE WCNC ; Conference date: 24-03-2025 Through 27-03-2025",

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Wu, Z , Bravalheri, AC, Parra Ullauri, JM, Wu, Y & Simeonidou, D 2025, Service-aware Maximum Likelihood-based Network Slicing for Live Low-latency Streaming. in 2025 IEEE Wireless Communications and Networking Conference (WCNC). IEEE Conference on Wireless Communications and Networking, Institute of Electrical and Electronics Engineers (IEEE), IEEE Wireless Communications and Networking Conference, Milan, Italy, 24/03/25. https://doi.org/10.1109/WCNC61545.2025.10978784

Service-aware Maximum Likelihood-based Network Slicing for Live Low-latency Streaming. / Wu, Zhaozhou ; Bravalheri, Anderson C; Parra Ullauri, Juan Marcelo et al.
2025 IEEE Wireless Communications and Networking Conference (WCNC). Institute of Electrical and Electronics Engineers (IEEE), 2025. (IEEE Conference on Wireless Communications and Networking).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Service-aware Maximum Likelihood-based Network Slicing for Live Low-latency Streaming

AU - Wu, Zhaozhou

AU - Bravalheri, Anderson C

AU - Parra Ullauri, Juan Marcelo

AU - Wu, Yulei

AU - Simeonidou, Dimitra

PY - 2025/5/9

Y1 - 2025/5/9

N2 - Network slicing (NS) is a promising solution for media services, such as live streaming in telecom networks. NS enables customised network conditions for different applications and requirements. This customisation granularity is further enhanced through the use of 5G Quality of Service (QoS) flows for intra-slice management. Given the fact that network slices are becoming more dedicated to specific services' quality requirements, it becomes important to find service-aware NS methods that guarantee service quality while minimising resource consumption. To this end, this paper presents a Maximum Likelihood-based Network Slicing (MaxLiNS) method that minimises resource consumption with guaranteed Quality of Experience (QoE) for live low-latency streaming service under playback buffer level estimation and control. We evaluated the MaxLiNS method against existing service-agnostic and emerging service-aware NS methods on our End-to-End (E2E) 5G testbed. The results show that the MaxLiNS outperforms existing NS methods in terms of quality guarantee and resource consumption minimisation.

AB - Network slicing (NS) is a promising solution for media services, such as live streaming in telecom networks. NS enables customised network conditions for different applications and requirements. This customisation granularity is further enhanced through the use of 5G Quality of Service (QoS) flows for intra-slice management. Given the fact that network slices are becoming more dedicated to specific services' quality requirements, it becomes important to find service-aware NS methods that guarantee service quality while minimising resource consumption. To this end, this paper presents a Maximum Likelihood-based Network Slicing (MaxLiNS) method that minimises resource consumption with guaranteed Quality of Experience (QoE) for live low-latency streaming service under playback buffer level estimation and control. We evaluated the MaxLiNS method against existing service-agnostic and emerging service-aware NS methods on our End-to-End (E2E) 5G testbed. The results show that the MaxLiNS outperforms existing NS methods in terms of quality guarantee and resource consumption minimisation.

KW - 5G/B5G

KW - Network Slicing

KW - Service-aware

KW - Live Low-latency Streaming

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U2 - 10.1109/WCNC61545.2025.10978784

DO - 10.1109/WCNC61545.2025.10978784

M3 - Conference Contribution (Conference Proceeding)

SN - 9798350368376

T3 - IEEE Conference on Wireless Communications and Networking

BT - 2025 IEEE Wireless Communications and Networking Conference (WCNC)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - IEEE Wireless Communications and Networking Conference

Y2 - 24 March 2025 through 27 March 2025

ER -

Wu Z , Bravalheri AC, Parra Ullauri JM, Wu Y , Simeonidou D. Service-aware Maximum Likelihood-based Network Slicing for Live Low-latency Streaming. In 2025 IEEE Wireless Communications and Networking Conference (WCNC). Institute of Electrical and Electronics Engineers (IEEE). 2025. (IEEE Conference on Wireless Communications and Networking). doi: 10.1109/WCNC61545.2025.10978784

Service-aware Maximum Likelihood-based Network Slicing for Live Low-latency Streaming

Abstract

Publication series

Conference

Research Groups and Themes

Keywords

Access to Document

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