The University of Southampton
University of Southampton Institutional Repository

Optimal-power superposition modulation for scalable video broadcasting

Optimal-power superposition modulation for scalable video broadcasting
Optimal-power superposition modulation for scalable video broadcasting
To mitigate the burden of the tele-traffic imposed by video streaming, Scalable Video Coding (SVC) is invoked for mapping the video clips to multiple layers, which allows us to improve the coverage quality. Although numerous nonorthogonal techniques have been conceived in the literature for maximizing the theoretical capacity relying on the idealized simplifying assumption of perfect channel coding. There is a paucity of practical finite-delay channel-coded solutions capable of mitigating the avalanche-like error proliferation routinely encountered in the face of hostile channels. Against this background, we propose SVC based Superposition Coding (SC) assisted video broadcasting, which curbs the error propagation introduced both by the inter-layer dependency and the Successive Interference Cancellation (SIC) required by the superimposed signal. Specifically, we formulate an Objective Function (OF) based on the average video quality across the Base Station’s (BS) coverage area and then determine the optimal power scaling coefficients of each video layer using a bespoke Evolutionary Algorithm (EA). Our solution strikes a compelling compromise between the best possible video service provided for the cellcentre and the cell-edge users. Explicitly, our simulation results show that the optimal-power SC system guarantees a better compromise than its Time Division Multiplexing (TDM) and conventional QAM assisted counterparts, despite its reduced receiver complexity
0018-9545
16230 - 16234
Zhang, Yanqing
b1cd478d-ba8c-4063-8a12-2b67945cee03
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Xu, Chao
5710a067-6320-4f5a-8689-7881f6c46252
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Zhang, Yanqing
b1cd478d-ba8c-4063-8a12-2b67945cee03
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Xu, Chao
5710a067-6320-4f5a-8689-7881f6c46252
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Zhang, Yanqing, Zhang, Jiankang, Xu, Chao, El-Hajjar, Mohammed and Hanzo, Lajos (2020) Optimal-power superposition modulation for scalable video broadcasting. IEEE Transactions on Vehicular Technology, 69, 16230 - 16234. (doi:10.1109/TVT.2020.3036529).

Record type: Article

Abstract

To mitigate the burden of the tele-traffic imposed by video streaming, Scalable Video Coding (SVC) is invoked for mapping the video clips to multiple layers, which allows us to improve the coverage quality. Although numerous nonorthogonal techniques have been conceived in the literature for maximizing the theoretical capacity relying on the idealized simplifying assumption of perfect channel coding. There is a paucity of practical finite-delay channel-coded solutions capable of mitigating the avalanche-like error proliferation routinely encountered in the face of hostile channels. Against this background, we propose SVC based Superposition Coding (SC) assisted video broadcasting, which curbs the error propagation introduced both by the inter-layer dependency and the Successive Interference Cancellation (SIC) required by the superimposed signal. Specifically, we formulate an Objective Function (OF) based on the average video quality across the Base Station’s (BS) coverage area and then determine the optimal power scaling coefficients of each video layer using a bespoke Evolutionary Algorithm (EA). Our solution strikes a compelling compromise between the best possible video service provided for the cellcentre and the cell-edge users. Explicitly, our simulation results show that the optimal-power SC system guarantees a better compromise than its Time Division Multiplexing (TDM) and conventional QAM assisted counterparts, despite its reduced receiver complexity

Text
final - Accepted Manuscript
Download (423kB)

More information

Accepted/In Press date: 4 November 2020
e-pub ahead of print date: 6 November 2020
Published date: 12 December 2020

Identifiers

Local EPrints ID: 444972
URI: http://eprints.soton.ac.uk/id/eprint/444972
ISSN: 0018-9545
PURE UUID: 549f5faa-2740-4f52-88b2-648db4c342cd
ORCID for Yanqing Zhang: ORCID iD orcid.org/0000-0003-2349-1925
ORCID for Jiankang Zhang: ORCID iD orcid.org/0000-0001-5316-1711
ORCID for Chao Xu: ORCID iD orcid.org/0000-0002-8423-0342
ORCID for Mohammed El-Hajjar: ORCID iD orcid.org/0000-0002-7987-1401
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 13 Nov 2020 17:31
Last modified: 23 Mar 2021 05:01

Export record

Altmetrics

Contributors

Author: Yanqing Zhang ORCID iD
Author: Jiankang Zhang ORCID iD
Author: Chao Xu ORCID iD
Author: Mohammed El-Hajjar ORCID iD
Author: Lajos Hanzo ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×