Semantic Video Communication via Multi-Scale Convolution and Dynamic Routing for Next-Generation Networks
Gengtian Shi, Jinze Yu, Chenhao Wu, Shaofei Wang, Eiji Fukuzawa, Junjie Tang, Hiroshi Onoda, Jiang Liu
Abstract
The exponential growth of video traffic demands novel semantic communication paradigms that transmit meaning rather than raw bits. We present a generative AI-enabled framework for semantic video communication addressing two critical challenges: efficient hierarchical temporal modeling for bandwidth-constrained transmission and robust semantic alignment between video content and natural language queries at network edge devices. Our approach introduces a multi-scale temporal convolutional encoder that captures motion patterns across different temporal granularities with O(T) complexity suitable for resource-constrained IoT deployments. We further propose a capsule-based dynamic routing mechanism that iteratively refines segment-query associations, enabling flexible modeling of non-monotonic semantic alignments essential for goal-oriented communication. These components are unified through a multi-task learning objective optimizing temporal boundary regression, cross-modal alignment, and capsule diversity. Experiments on ActivityNet Captions demonstrate significant improvements, achieving 42.9% Recall@0.5 and 41.1% mean IoU while maintaining computational efficiency critical for edge deployment.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.