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This work aims to enable efficient digital rights management for volumetric video by introducing attribute-based selective coordinate encryption for point clouds. The method encrypts only a subset of coordinates to reduce computation and latency while maintaining security. Selective encryption makes point cloud frames distorted enough to block meaningful unauthorized viewing while still allowing basic visibility. The framework allows variation in the amount and type of encrypted coordinates (X, Y, Z, or combinations). Visual degradation is measured using standard point cloud quality metrics. Results show that encrypting only X coordinates reduces encryption time by 37% and decryption time by 46% compared to full encryption. Encrypting X and Y reduces these times by 20% and 36% while still degrading visual quality. Attribute-Based Encryption allows protected content to be cached and distributed without re-encryption, which reduces computation and latency. The current evaluation covers individual frames. Future work will cover full volumetric video streams and analyze caching gains during streaming with Attribute-Based Encryption. 

This paper presents an alternate method for encrypting video streams using attribute-based encryption, focusing on securing the data rather than the connection between streaming endpoints. This shift allows video segments to be encrypted once at the source and cached anywhere, removing the need for per-client encryption and decryption at intermediate caches. Access can be restricted or revoked by disabling users’ private keys instead of re-encrypting the entire video stream. The approach also removes the need for decryption and re-encryption at caches, since encrypted content can be stored directly. The work introduces ABEVS, a framework that applies attribute-based encryption to DRM-enabled video streaming. ABE is integrated into a DASH-based streaming client, and video content is encrypted and distributed through intermediate caches. The system is evaluated on CloudLab. Results show that the approach reduces computational load on caches and has minimal impact on cache hit rate and video quality compared to HTTPS-based streaming. 

Accessing high-quality video content can be challenging due to insufficient and unstable network bandwidth. Recent advances in neural enhancement have shown promising results in improving the quality of degraded videos through deep learning. Neural-Enhanced Streaming (NES) incorporates this new approach into video streaming, allowing users to download low-quality video segments and then enhance them to obtain high-quality content without violating the playback of the video stream. We introduce BONES, an NES control algorithm that jointly manages the network and computational resources to maximize the quality of experience (QoE) of the user. BONES formulates NES as a Lyapunov optimization problem and solves it in an online manner with near-optimal performance, making it the first NES algorithm to provide a theoretical performance guarantee. Comprehensive experimental results indicate that BONES increases QoE by 5% to 20% over state-of-the-art algorithms with minimal overhead. Our code is available at https://github.com/UMass-LIDS/bones.