面向智能反射面辅助的无线视频软传输联合资源优化算法

吴俊杰; 罗雷; 朱策; 江沛

doi:10.11999/JEIT250019

面向智能反射面辅助的无线视频软传输联合资源优化算法

doi: 10.11999/JEIT250019 cstr: 32379.14.JEIT250019

吴俊杰¹,
罗雷^{1, 2, ,},
朱策²,
江沛³

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
电子科技大学信息与通信工程学院成都 611731
3.
重庆大学机械与运载工程学院重庆 400044

基金项目: 国家自然科学基金(U19A2052, 62020106011, 62061015)，重庆市自然科学基金(CSTB2022NSCQ-MSX1283, 2023NSCQ-MSX2930)，重庆邮电大学青创基金(SCIE-QN-2022-05)

详细信息

作者简介:
吴俊杰：男，博士生，研究方向为无线视频编码与传输、多媒体信号处理、深度学习等

罗雷：男，副教授，研究方向为视频编解码、多媒体信号处理、深度学习、机器学习等

朱策：男，教授，研究方向为视频图像处理等

江沛：男，副教授，研究方向为机器人控制理论等

通讯作者:
罗雷　luolei@cqupt.edu.cn

中图分类号: TN92
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- 被引次数: 0
出版历程
- 收稿日期: 2025-01-10
- 修回日期: 2025-07-02
- 网络出版日期: 2025-07-07
- 刊出日期: 2025-08-27

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

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)

摘要

摘要: 智能反射面(IRS)是下一代移动通信系统的关键使能技术之一，以应对海量设备接入与海量数据流量的需求。而视频数据占比超过了移动数据流量的80%，且呈现稳步增长的趋势。因此，更为高效的无线视频传输方案也成为下一代移动通信系统的迫切需要。为此，该文提出一种面向智能反射面辅助的无线视频软传输方案，充分利用智能反射面辅助的次链路对所传输的视频信号进行增强。所提方案中，视频信号的传输失真同时受到主次链路3种无线资源的影响，分别是传输功率、主发射机的有源波束成形和智能反射面的无源波束成形。视频传输失真最小化问题被建模为联合资源优化问题，并采用交替优化方法将多元联合优化问题解耦为多个单变量优化子问题逐一求解。仿真结果表明，该文所提方法相较于已有的视频软传输方法，峰均功率比(PSNR)提升至少约1.82 dB，显著提高了接收视频重建质量和无线视频传输效率。
- 视频软传输 /
- 智能反射面 /
- 功率分配 /
- 波束成形 /
- 联合资源优化
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

HTML全文

图 1 所提面向智能反射面辅助的视频软传输系统框图

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图 2 主次信号传输周期关系

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图 3 本文方法与其他方法的视频传输性能比较

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图 4 本文方法与单链路AGCC-SoftCast方法的主观质量对比，CSNR = 5 dB

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图 5 次链路信道质量的影响(序列：BQTerrace, CSNR = 5 dB)

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图 6 不同次信号$c$的影响 (序列：BQTerrace)

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图 7 IRS反射单元数量的影响及次主链路的视频信号可达速率之比，CSNR = 5 dB

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图 8 1个GOP内不同视频帧的接收质量，CSNR = 5 dB

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图 9 不同GOP参数的影响，序列：BQTerrace, CSNR = 5 dB

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1 求解联合优化$ {\text{P1}} $的AO交替优化算法

初始化：收敛精度$\varepsilon = 0.001$，最大迭代次数$K$，次信号$c$的调　制方式，有源波束成形向量随机初始值${{\boldsymbol{w}}^{(0)}}$，功率分配因子随　机初始值$\mu _i^{(0)}$；
(1) for $k$ = 1 to $K$ do
(2) 　通过${{\boldsymbol{w}}^{(k - 1)}}$和$\mu _i^{(k - 1)}$，利用SDR方法求解问题$ {\text{P1}}{\text{.1}} $得到　　　 ${{\boldsymbol{\theta }}^{(k)}}$；
(3) 　通过$\mu _i^{(k - 1)}$和更新的${{\boldsymbol{\theta }}^{(k)}}$，利用SDR方法求解问题$ {\text{P1}}{\text{.2}} $得　　　到${{\boldsymbol{w}}^{(k)}}$；
(4) 　通过更新${{\boldsymbol{\theta }}^{(k)}}$和${{\boldsymbol{w}}^{(k)}}$，利用拉格朗日乘子法得到$\mu _i^{(k)}$；
(5) 　基于${{\boldsymbol{\theta }}^{(k)}}$, ${{\boldsymbol{w}}^{(k)}}$和$\mu _i^{(k)}$计算第$k$次失真$D_{{\text{toatal}}}^{(k)}$；
(6) 　if $D_{{\text{toatal}}}^{(k - 1)} - D_{{\text{toatal}}}^{(k)} \le \varepsilon $, then
(7) 　　返回${{\boldsymbol{\theta }}^{(k)}}$, ${{\boldsymbol{w}}^{(k)}}$和$\mu _i^{(k)}$；
(8) 　else if $k = K$, then
(9) 　　返回${{\boldsymbol{\theta }}^{(K)}}$,${{\boldsymbol{w}}^{(K)}}$和$\mu _i^{(K)}$
(10) end if
(11) end for
(12) 输出：${{\boldsymbol{\theta }}^{({\text{opt}})}}$, ${{\boldsymbol{w}}^{({\text{opt}})}}$和$\mu _i^{({\text{opt}})}$

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表 1 在HEVC标准测试序列中的算法性能对比(CSNR = 5 dB，性能指标：PSNR(dB))

类型	分辨率	序列名称	本文方法	ACIB-SoftCast	SoftCast-IDCT	AGCC-SoftCast	ParCast
Class A	2 560×1 600	PeopleOnStreet	32.11	30.55	29.48	29.10	26.66
Class A	2 560×1 600	Traffic	34.29	32.67	31.80	31.55	29.29
Class B	1 920×1 080	BQTerrace	29.90	28.91	27.51	26.75	24.12
		Cactus	32.18	30.38	29.94	29.78	26.78
		Kimono	37.49	36.09	35.29	35.03	31.89
		ParkScene	35.26	33.37	32.11	32.00	30.06
		Tennis	36.08	34.56	33.98	33.08	30.98
Class C	832×480	BasketballDrill	32.82	31.82	29.95	29.96	27.32
		BQMall	31.38	29.57	28.89	28.13	26.38
		PartyScene	30.01	28.21	27.91	27.41	26.11
		RaceHorses-C	30.22	28.27	27.72	27.58	24.68
Class D	416×240	BasketballPass	31.98	30.16	29.78	28.49	26.99
		BlowingBubbles	31.45	29.32	28.51	27.95	25.89
		BQSquare	27.90	26.78	25.34	24.75	23.19
Class E	1 280×720	KristenAndSara	34.99	33.25	32.12	31.78	29.05
Class E	1 280×720	Johnny	35.11	33.25	32.62	31.49	28.61
Class F	1 024×768	ChinaSpeed	29.66	29.15	28.06	26.16	25.37
Class F	1 280×720	SlideEditing	27.72	27.10	25.59	25.41	24.97
平均性能			32.25	30.75	29.81	29.24	27.13

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