Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications

Abstract:Immersive formats such as 360° and 6DoF point cloud videos require high bandwidth and low latency, posing challenges for real-time AR/VR streaming. This work focuses on reducing bandwidth consumption and encryption/decryption delay, two key contributors to overall latency. We design a system that downsamples point cloud content at the origin server and applies partial encryption. At the client, the content is decrypted and upscaled using an ML-based super-resolution model. Our evaluation demonstrates a nearly linear reduction in bandwidth/latency, and encryption/decryption overhead with lower downsampling resolutions, while the super-resolution model effectively reconstructs the original full-resolution point clouds with minimal error and modest inference time.

Cryptography and Security (cs.CR); Machine Learning (cs.LG); Multimedia (cs.MM); Image and Video Processing (eess.IV)

arXiv:2512.15823 [cs.CR]

(or arXiv:2512.15823v3 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2512.15823 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/ISM66958.2025.00035 Focus to learn more DOI(s) linking to related resources

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