1. Radiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse
- Author
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Dopiriak, Matúš, Šlapak, Eugen, Gazda, Juraj, Gurjar, Devendra S., Faruque, Mohammad Abdullah Al, and Levorato, Marco
- Subjects
Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Information Theory ,Computer Science - Networking and Internet Architecture - Abstract
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 scene in compressed form. This method extracts differences between CAV-frame capturing actual traffic and RF-frame capturing empty scene from the same pose in batches encoded and transmitted over the MEC network. Experiments show data savings up to 90% against H.264 codec and 45% against H.265 codec under different conditions as lighting changes, rain and fog. RFDVC exhibits greater resilience to transmission errors, achieving a bit error rate (BER) of approximately 10^-1.6 at signal-to-noise ratio (SNR) of 10 dB, compared to 10^-1.4 for standard video coding.
- Published
- 2024