Joint Resource Optimization Algorithm for Intelligent Reflective Surface Assisted Wireless Soft Video Transmission

WU Junjie; LUO Lei; ZHU Ce; JIANG Pei

doi:10.11999/JEIT250019

Volume 47 Issue 8

Aug. 2025

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WU Junjie, LUO Lei, ZHU Ce, JIANG Pei. Joint Resource Optimization Algorithm for Intelligent Reflective Surface Assisted Wireless Soft Video Transmission[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2630-2641. doi: 10.11999/JEIT250019

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WU Junjie, LUO Lei, ZHU Ce, JIANG Pei. Joint Resource Optimization Algorithm for Intelligent Reflective Surface Assisted Wireless Soft Video Transmission[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2630-2641. doi: 10.11999/JEIT250019

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Joint Resource Optimization Algorithm for Intelligent Reflective Surface Assisted Wireless Soft Video Transmission

doi: 10.11999/JEIT250019 cstr: 32379.14.JEIT250019

WU Junjie¹,
LUO Lei^{1, 2
,
,},
ZHU Ce²,
JIANG Pei³

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
3.
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China

Funds: The National Natural Science Foundation of China(U19A2052, 62020106011, 62061015), The Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX1283, 2023NSCQ-MSX2930), The Youth Innovation Group Support Program of ICE Discipline of CQUPT (SCIE-QN-2022-05)

Received Date: 2025-01-10
Rev Recd Date: 2025-07-02

Available Online: 2025-07-07

Publish Date: 2025-08-27

Abstract

Abstract

Objective Intelligent Reflecting Surface (IRS) technology is a key enabler for next-generation mobile communication systems, addressing the growing demands for massive device connectivity and increasing data traffic. Video data accounts for over 80% of global mobile traffic, and this proportion continues to rise. Although video SoftCast offers a simpler structure and more graceful degradation compared to conventional separate source-channel coding schemes, its transmission efficiency is restricted by the limited availability of wireless transmission resources. Moreover, existing SoftCast frameworks are not inherently compatible with IRS-assisted wireless channels. To address these limitations, this paper proposes an IRS-assisted wireless soft video transmission scheme. Methods Video soft transmission distortion is jointly determined by three critical wireless resources: transmit power, active beamforming at the primary transmitter, and passive beamforming at the IRS. Minimizing video soft transmission distortion is therefore formulated as a joint optimization problem over these resources. To solve this multivariable problem, an Alternating Optimization (AO) framework is employed to decouple the original problem into single-variable subproblems. For the fractional nonhomogeneous quadratic optimization and unit-modulus constraints arising in this process, the Semi-Definite Relaxation (SDR) method is applied to obtain near-optimal solutions for both active and passive beamforming vectors. Based on the derived beamforming vectors, the optimal power allocation factor for soft transmission is then computed using the Lagrange multiplier method. Results and Discussions Simulation results indicate that the proposed method yields an improvement of at least 1.82 dB in Peak Signal-to-Noise Ratio (PSNR) compared to existing video soft transmission approaches (Fig. 3). Besides, evaluation across extensive HEVC test sequences shows that the proposed method achieves an average received quality gain of no less than 1.51 dB (Table 1). Further simulations reveal that when the secondary link channel quality falls below a critical threshold, it no longer contributes to improving the received video quality (Fig. 5). Rapid variations in the secondary signal $c$ degrade the reception quality of the primary signal, with a reduction of approximately 0.52 dB observed (Fig. 6). Increasing the number of IRS elements significantly enhances both video reception quality and achievable rates for the primary and secondary links (Fig. 7); however, this improvement comes with a power-law scaling increase in computational complexity. Additional simulations confirm that the proposed method maintains per-frame quality fluctuations within an acceptable range across each Group Of Pictures (GOP) (Fig. 8). As GOP size increases, temporal redundancy within the source is more effectively removed, leading to further improvements in received quality, although this is accompanied by higher computational complexity (Fig. 9). Conclusions This paper proposes an IRS-assisted soft video transmission scheme that leverages IRS-aided secondary links to improve received video quality. To minimize video signal distortion, a multivariable optimization problem is formulated for joint resource allocation. An AO framework is adopted to decouple the problem into single-variable subproblems, which are solved iteratively. Simulation results show that the proposed method achieves significant improvements in both objective and subjective visual quality compared to existing video transmission algorithms. In addition, the effects of secondary link channel gain, secondary signal characteristics, the number of IRS elements, and GOP parameters on transmission performance are systematically examined. This study demonstrates, for the first time, the performance enhancement of video soft transmission using IRS and provides a technical basis for the development of video soft transmission in IRS-assisted communication environments.
- Video soft transmission,
- Intelligent Reflecting Surface (IRS),
- Power allocation,
- Beamforming,
- Joint resource optimization

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References(31)

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