| Citation: | LI Y，WAN Z，DENG C Z，et al. Edge-intelligent transmission optimization of emergency surveillance video based on IcD-FDRL[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（7）：2314-2329 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0378
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Emergency surveillance video transmission is a key technical means to improve emergency handling capability under circumstances such as emergency monitoring, public security incident handling, and post-disaster reconstruction. It has gradually become a key focus of research and development in the construction of the national smart emergency system. With the continuous development of 5G technology and decision-making artificial intelligence technology in recent years, an edge-intelligent transmission architecture for emergency surveillance video was established, aimed at public safety and emergency rescue monitoring in local areas. This model seeks to achieve adaptive and high-quality transmission of emergency surveillance video. Furthermore, the importance measurement method of emergency surveillance video was designed, and an intra-clustered dynamic federated deep reinforcement learning algorithm was proposed. The proposed optimization method based on intra-clustered dynamic federated deeps reinforcement learning (IcD-FDRL) enhances the edge-intelligent transmission of emergency surveillance video, breaks monitoring data silos, improves algorithm learning efficiency, and realizes low-delay, low-cost, high-quality, and priority transmission of important emergency surveillance video. Finally, a simulation experiment was performed and its results were compared, verifying the effectiveness of the proposed model and algorithms.
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