Octavius: Towards Efficient Transmission of 3D Point Clouds Via Adaptive Encoding and QUIC
Muhammad Haseeb, Eugene Chai, Matteo Varvello · 2025
The popularity of point cloud data is rising, driven by applications in virtual reality, urban planning, and volumetric videos. Real-time streaming of point clouds is challenging due to their large size. Current solutions focus on viewport adaptation, with less emphasis on transcoding, essential for streaming to diverse clients. Traditional transcoding involves creating multiple bitrate versions through subsampling and encoding, which is computationally intensive and storage-heavy. These methods also lack agility, limiting clients to predefined bitrates. Octavius advocates for an agile and scalable transcoding scheme for point clouds, requiring minimal computational resources and storage. By strategically withholding some data from the last layer of an octree -- a popular encoding data structure for point clouds -- clients can still decode the point cloud, with quality degradation based on the withheld data. This enables a continuous set of bitrates to be generated on-the-fly by simply withholding some amount of data during transmission. Octavius further uses a smart packing (for making packets) scheme to evenly distribute video degradation in event of consecutive packets withheld or missing and leverages a QUIC-based mixed-reliability protocol to reduce latency, avoiding packet retransmissions.