A Semantic-Aware Video Offloading Framework for Bandwidth-Efficient Cloud-Based Surveillance

The proliferation of IoT-based surveillance has caused a sharp rise in video data, straining network bandwidth and cloud storage. Conventional video compression exploits pixel-level redundancy but ignores the semantic importance of content, transmitting large volumes of redundant background. This paper proposes a semantic-aware video offloading framework that improves bandwidth efficiency in cloud-based surveillance. DeepLabV3+ with a ResNet-50 backbone performs semantic segmentation at the edge to extract relevant foreground objects (e.g., pedestrians and vehicles) while suppressing static background. A background reference caching mechanism transmits the static scene once and reuses it at the cloud for full-frame reconstruction, minimizing redundant transmission. On a dataset of 12 surveillance sequences (self-captured videos plus sequences from the CDnet 2014 benchmark), the method achieves up to 74.63% reduction in transmitted data, a 33% improvement in storage efficiency, and a compression ratio of 2.88×, while maintaining an average PSNR of 44.92 dB. Paired t-tests (p<0.001) and sensitivity analysis across varying scene dynamics and semantic configurations confirm the robustness of the approach, and comparisons indicate clear gains over conventional motion-based offloading in bandwidth efficiency and reconstruction fidelity.