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, achieving up to +0.29 structural similarity index measure (SSIM) improvement at block error rate (BLER) = 0.35 in non-rainy and +0.25 at BLER = 0.2 in rainy conditions, ensuring superior visual quality compared to standard video coding (VC) methods across various conditions.

翻译：联网自动驾驶车辆（CAV）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，从而支持车载元宇宙内的应用。该元宇宙将物理环境转化为数字空间，以实现高级分析或预测建模。一个核心挑战是通过数字孪生（DT）实现物理到虚拟（P2V）的同步，这依赖于MEC网络和超可靠低时延通信（URLLC）。为此，我们提出辐射场（RF）差分视频压缩（RFDVC）方法，该方法采用RF编码器和RF解码器架构，利用分布式辐射场作为数字孪生，以压缩形式存储逼真的3D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，并在MEC网络上进行编码和传输。实验表明，在不同条件（如光照变化和雨天）下，相较于H.264编解码器，该方法可节省高达71%的数据量；相较于H.265编解码器，可节省44%的数据量。RFDVC还展现出对传输错误的鲁棒性：在非雨天条件下，当块错误率（BLER）=0.35时，结构相似性指数（SSIM）提升高达+0.29；在雨天条件下，当BLER=0.2时，SSIM提升高达+0.25。这确保了在各种条件下，相较于标准视频编码（VC）方法，RFDVC能提供更优的视觉质量。