Low-latency visual information sharing is a key enabler for cooperative perception in vehicular networks. Network coding (NC) can exploit wireless superposition and side information to improve spectral efficiency in bidirectional relaying. This paper presents an end-to-end learned framework for Roadside Units (RSU)-assisted bidirectional view sharing that integrates joint source-channel coding (JSCC) with a feature-domain, self-information-assisted NC scheme over learned semantic representations, referred to as semantic network coding (semantic NC). In the proposed framework, two vehicles encode their camera images into compact semantic features and simultaneously transmit them to the RSU. The RSU exploits signal additivity to form a feature-domain mixture and broadcasts the mixed representation back to both vehicles. Each vehicle then uses its own transmitted semantic feature as self-information to cancel its contribution from the received mixture and reconstruct the other vehicle’s view through a neural decoder. Experiments under AWGN and Rayleigh fading channels show that the proposed semantic NC scheme achieves stable reconstruction performance across different SNRs. Compared with semantic transmission without NC, the proposed semantic NC incurs about 0.3–1.5 dB PSNR loss in the KITTI high-resolution setting and about 0.9–2.2 dB PSNR loss in the CIFAR-10 low-resolution setting, while reducing the required bidirectional relay transmission phases from four time slots to two. These results demonstrate that the proposed scheme achieves a favorable reconstruction–latency trade-off and has potential for low-latency, reconstruction-oriented view sharing in vehicular networks.
Wang et al. (Tue,) studied this question.