AI-Based Models for Video Compression and Adaptive Transmission in IoT Surveillance over Optical Networks

In order to overcome the difficulties caused by constrained network and computational resources, intelligent surveillance systems inside the Internet of Things (IoT) infrastructure are increasingly depending on sophisticated video compression and adaptive data transfer techniques. This study presents integrated models that combine AI-assisted adaptive transmission techniques designed for optical IoT contexts with high-efficiency codecs like HEVC and VVC. The suggested approach places a strong emphasis on context-aware video processing, edge computing, and real-time optimization. Experiments verify notable enhancements in system scalability, transmission delay reduction, and quality retention.

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