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arXiv · 2512.15823

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

Mohammad Waquas Usmani·Sankalpa Timilsina·Michael Zink·Susmit Shannigrahi
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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.

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Mohammad Waquas Usmani, Sankalpa Timilsina, Michael Zink, Susmit Shannigrahi. 2026-09-02. Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications. https://doi.org/10.1109/ism66958.2025.00035

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