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
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
12 December 2020
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, .
(doi:10.1109/TVT.2020.3036529).
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
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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
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Date deposited: 13 Nov 2020 17:31
Last modified: 18 Mar 2024 05:15
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Author:
Yanqing Zhang
Author:
Jiankang Zhang
Author:
Chao Xu
Author:
Mohammed El-Hajjar
Author:
Lajos Hanzo
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