Radiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908600914411520 |
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| author | Dopiriak, Matúš Šlapak, Eugen Gazda, Juraj Gurjar, Devendra Singh Faruque, Mohammad Abdullah Al Levorato, Marco |
| author_facet | Dopiriak, Matúš Šlapak, Eugen Gazda, Juraj Gurjar, Devendra Singh Faruque, Mohammad Abdullah Al Levorato, Marco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11857 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Radiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse Dopiriak, Matúš Šlapak, Eugen Gazda, Juraj Gurjar, Devendra Singh Faruque, Mohammad Abdullah Al Levorato, Marco Signal Processing Information Theory Networking and Internet Architecture 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. |
| title | Radiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse |
| topic | Signal Processing Information Theory Networking and Internet Architecture |
| url | https://arxiv.org/abs/2411.11857 |