Connected and autonomous vehicles (CAVs) offload computationally intensive tasks to multi-access edge computing (MEC) servers via vehicle-to-infrastructure (V2I) communication, enabling applications within the vehicular metaverse, which transforms physical environment into the digital space enabling advanced analysis or predictive modeling. A core challenge is physical-to-virtual (P2V) synchronization through digital twins (DTs), reliant on MEC networks and ultra-reliable low-latency communication (URLLC). To address this, we introduce radiance field (RF) delta video compression (RFDVC), which uses RF-encoder and RF-decoder architecture using distributed RFs as DTs storing photorealistic 3D urban scenes in compressed form. This method extracts differences between CAV-frame capturing actual traffic and RF-frame capturing empty scene from the same camera pose in batches encoded and transmitted over the MEC network. Experiments show data savings up to 71% against H.264 codec and 44% against H.265 codec under different conditions as lighting changes, and rain. RFDVC also demonstrates resilience to transmission errors, significantly outperforming the standard codec in non-rainy conditions with up to a +0.26 structural similarity index measure (SSIM) improvement over H.264 codec, and maintaining a +0.18 SSIM improvement even in challenging rainy conditions, both measured at a block error rate (BLER) of 0.25.

翻译：联网自动驾驶车辆(CAVs)通过车对基础设施(V2I)通信将计算密集型任务卸载至多接入边缘计算(MEC)服务器，从而支撑车载元宇宙应用——该体系将物理环境转化为数字空间以实现高级分析或预测建模。通过数字孪生(DTs)实现物理-虚拟(P2V)同步是核心挑战，其依赖于MEC网络与超高可靠低时延通信(URLLC)。为此，本文提出辐射场差分视频压缩(RFDVC)方法，采用以分布式辐射场作为数字孪生的RF编码器-解码器架构，以压缩形式存储逼真的三维城市场景。该方法批量提取CAV帧（捕获实际交通）与RF帧（从相同相机位姿捕获空场景）之间的差异，通过MEC网络进行编码传输。实验表明：在光照变化及雨天等不同条件下，相比H.264编解码器最高可节省71%数据量，相比H.265编解码器最高可节省44%。RFDVC还展现出对传输错误的鲁棒性：在非雨天条件下以0.25误块率(BLER)测量时，其结构相似性指数(SSIM)较H.264编解码器最高提升+0.26；在具有挑战性的雨天条件下仍保持+0.18的SSIM提升，均显著优于标准编解码器。