Guess and Send: World-Model-Guided Semantic Packet Transmission for Robust Video Perception

Abstract

Reliable video perception over bandwidth-constrained wireless links requires transmitting information that the receiver cannot already recover from its evolving scene representation. This paper introduces Guess and Send, a world-model-guided semantic packet transmission framework that prioritizes information according to its contribution to the receiver's perception. A shared feature-space world model predicts the receiver's scene state through affine motion compensation and learned residual displacement, while sender-side verification identifies reusable predictions. An acknowledgment-synchronized receiver twin estimates task-impact scores through two counterfactual evaluations, guiding packet selection and unequal error protection over LDPC-coded wireless channels. A compact 3-bit feature representation increases spatial coverage under stringent bandwidth constraints, while predictive fallback sustains perception during packet outages. Experiments on KITTI-MOTS (KITTI) and VisDrone2019-MOT (VisDrone) under Rayleigh fading and AWGN demonstrate substantial gains in low-SNR conditions. On KITTI at a 4.13% reference bandwidth budget, Guess and Send achieves 47.5% J&F at 0 dB Rayleigh SNR, outperforming H.264 by 11.3 percentage points. Controlled ablations show that world-model prediction contributes up to 8.7 points under Rayleigh fading. Object-detection experiments further demonstrate the transferability of receiver-relative predictive transmission across visual perception tasks. These results establish world-model-guided, receiver-state-aware transmission as an effective approach to robust video perception under severe communication constraints.