Real-time video adaptation in virtualised 5G networks

Pablo Salva-Garcia; Jose M. Alcaraz-Calero; Qi Wang; Maria Barros; Anastasius Gavras

doi:10.1109/LCN44214.2019.8990815

Real-time video adaptation in virtualised 5G networks

Pablo Salva-Garcia, Jose M. Alcaraz-Calero, Qi Wang, Maria Barros, Anastasius Gavras

School of Computing, Engineering and Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

135 Downloads (Pure)

Abstract

Video applications are expected to increasingly dominate the traffic of mobile networks in the 5G era, and thus a real-time adaptation of these high resource demanding network applications is crucial in optimising the overall 5G networks. In this manuscript, we leverage Virtual Network Function (VNF) techniques to implement a video adaptation service that automatically adapts the quality of video transmissions depending on the status of the network. Furthermore, Network Function Virtualisation (NFV) techniques are employed here to simplify, optimise and speed up the deployment process of the aforementioned video adapter service (vAdapter), and therefore, allowing its on-demand deployment in a flexible way. We design, implement and test the scheme in a realistic virtualised 5G testbed. Empirical results focus on the scalability evaluation and performance as well as demonstrates a significant bandwidth reduction without compromising the final user’s video quality expectations.

Original language	English
Title of host publication	Proceedings of the 44th Annual IEEE Conference on Local Computer Networks
Subtitle of host publication	October 14-17, 2019, Osnabrück, Germany
Editors	Karl Andersson, Hwee-Pink Tan, Sharief Oteafy
Publisher	IEEE
ISBN (Electronic)	9781728110271
ISBN (Print)	9781728110288
DOIs	https://doi.org/10.1109/LCN44214.2019.8990815
Publication status	Published - 13 Feb 2020
Event	IEEE Conference on Local Computer Networks - Osnabrück, Germany Duration: 14 Oct 2019 → 17 Oct 2019 Conference number: 44 https://www.ieeelcn.org/prior/LCN44/index.html

Publication series

Name	Conference on Local Computer Networks
Publisher	IEEE
ISSN (Print)	0742-1303

Conference

Conference	IEEE Conference on Local Computer Networks
Abbreviated title	LCN 2019
Country/Territory	Germany
City	Osnabrück
Period	14/10/19 → 17/10/19
Internet address	https://www.ieeelcn.org/prior/LCN44/index.html

Keywords

Real-time
Multimedia
Adaptive
SDN
NFV
5G
Network management
QoS

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/LCN44214.2019.8990815

2019 07 08 Salva-Garcia et al Video acceptedAccepted author manuscript, 679 KB

Cite this

Salva-Garcia, P., Alcaraz-Calero, J. M., Wang, Q., Barros, M., & Gavras, A. (2020). Real-time video adaptation in virtualised 5G networks. In K. Andersson, H.-P. Tan, & S. Oteafy (Eds.), Proceedings of the 44th Annual IEEE Conference on Local Computer Networks: October 14-17, 2019, Osnabrück, Germany (Conference on Local Computer Networks). IEEE. https://doi.org/10.1109/LCN44214.2019.8990815

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title = "Real-time video adaptation in virtualised 5G networks",

abstract = "Video applications are expected to increasingly dominate the traffic of mobile networks in the 5G era, and thus a real-time adaptation of these high resource demanding network applications is crucial in optimising the overall 5G networks. In this manuscript, we leverage Virtual Network Function (VNF) techniques to implement a video adaptation service that automatically adapts the quality of video transmissions depending on the status of the network. Furthermore, Network Function Virtualisation (NFV) techniques are employed here to simplify, optimise and speed up the deployment process of the aforementioned video adapter service (vAdapter), and therefore, allowing its on-demand deployment in a flexible way. We design, implement and test the scheme in a realistic virtualised 5G testbed. Empirical results focus on the scalability evaluation and performance as well as demonstrates a significant bandwidth reduction without compromising the final user{\textquoteright}s video quality expectations.",

keywords = "Real-time, Multimedia, Adaptive, SDN, NFV, 5G, Network management, QoS",

author = "Pablo Salva-Garcia and Alcaraz-Calero, {Jose M.} and Qi Wang and Maria Barros and Anastasius Gavras",

year = "2020",

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Salva-Garcia, P , Alcaraz-Calero, JM , Wang, Q, Barros, M & Gavras, A 2020, Real-time video adaptation in virtualised 5G networks. in K Andersson, H-P Tan & S Oteafy (eds), Proceedings of the 44th Annual IEEE Conference on Local Computer Networks: October 14-17, 2019, Osnabrück, Germany. Conference on Local Computer Networks, IEEE, IEEE Conference on Local Computer Networks, Osnabrück, Lower Saxony, Germany, 14/10/19. https://doi.org/10.1109/LCN44214.2019.8990815

Real-time video adaptation in virtualised 5G networks. / Salva-Garcia, Pablo ; Alcaraz-Calero, Jose M.; Wang, Qi et al.
Proceedings of the 44th Annual IEEE Conference on Local Computer Networks: October 14-17, 2019, Osnabrück, Germany. ed. / Karl Andersson; Hwee-Pink Tan; Sharief Oteafy. IEEE, 2020. (Conference on Local Computer Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Real-time video adaptation in virtualised 5G networks

AU - Salva-Garcia, Pablo

AU - Alcaraz-Calero, Jose M.

AU - Wang, Qi

AU - Barros, Maria

AU - Gavras, Anastasius

N1 - Conference code: 44

PY - 2020/2/13

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N2 - Video applications are expected to increasingly dominate the traffic of mobile networks in the 5G era, and thus a real-time adaptation of these high resource demanding network applications is crucial in optimising the overall 5G networks. In this manuscript, we leverage Virtual Network Function (VNF) techniques to implement a video adaptation service that automatically adapts the quality of video transmissions depending on the status of the network. Furthermore, Network Function Virtualisation (NFV) techniques are employed here to simplify, optimise and speed up the deployment process of the aforementioned video adapter service (vAdapter), and therefore, allowing its on-demand deployment in a flexible way. We design, implement and test the scheme in a realistic virtualised 5G testbed. Empirical results focus on the scalability evaluation and performance as well as demonstrates a significant bandwidth reduction without compromising the final user’s video quality expectations.

AB - Video applications are expected to increasingly dominate the traffic of mobile networks in the 5G era, and thus a real-time adaptation of these high resource demanding network applications is crucial in optimising the overall 5G networks. In this manuscript, we leverage Virtual Network Function (VNF) techniques to implement a video adaptation service that automatically adapts the quality of video transmissions depending on the status of the network. Furthermore, Network Function Virtualisation (NFV) techniques are employed here to simplify, optimise and speed up the deployment process of the aforementioned video adapter service (vAdapter), and therefore, allowing its on-demand deployment in a flexible way. We design, implement and test the scheme in a realistic virtualised 5G testbed. Empirical results focus on the scalability evaluation and performance as well as demonstrates a significant bandwidth reduction without compromising the final user’s video quality expectations.

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KW - Multimedia

KW - Adaptive

KW - SDN

KW - NFV

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KW - Network management

KW - QoS

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DO - 10.1109/LCN44214.2019.8990815

M3 - Conference contribution

SN - 9781728110288

T3 - Conference on Local Computer Networks

BT - Proceedings of the 44th Annual IEEE Conference on Local Computer Networks

A2 - Andersson, Karl

A2 - Tan, Hwee-Pink

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PB - IEEE

T2 - IEEE Conference on Local Computer Networks

Y2 - 14 October 2019 through 17 October 2019

ER -

Real-time video adaptation in virtualised 5G networks

Abstract

Publication series

Conference

Keywords

UN SDGs

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Cite this