Optimal-Power Superposition Modulation for Scalable Video Broadcasting
Authors: Zhang, Y., Zhang, J., Xu, C., El-Hajjar, M. and Hanzo, L.
Journal: IEEE Transactions on Vehicular Technology
Volume: 69
Issue: 12
Pages: 16230-16234
eISSN: 1939-9359
ISSN: 0018-9545
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 non-orthogonal 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 cell-centre 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.
Source: Scopus
Optimal-Power Superposition Modulation for Scalable Video Broadcasting
Authors: Zhang, Y., Zhang, J., Xu, C., El-Hajjar, M. and Hanzo, L.
Journal: IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume: 69
Issue: 12
Pages: 16230-16234
eISSN: 1939-9359
ISSN: 0018-9545
DOI: 10.1109/TVT.2020.3036529
Source: Web of Science (Lite)