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.

翻译：360°视频和六自由度点云视频等沉浸式格式需要高带宽和低延迟，这对实时增强现实/虚拟现实流媒体提出了挑战。本研究聚焦于降低带宽消耗以及加密/解密延迟——这两个导致整体延迟的关键因素。我们设计了一个系统，在源服务器对点云内容进行下采样并应用部分加密。在客户端，内容被解密后使用基于机器学习的超分辨率模型进行上采样。评估结果表明，随着下采样分辨率的降低，带宽/延迟以及加密/解密开销呈近似线性下降，同时超分辨率模型能以最小的误差和适度的推理时间有效重建原始全分辨率点云。