Adaptive Rate Allocation for View-Aware Point-Cloud Streaming

TL;DR

This paper introduces an adaptive rate allocation algorithm for view-dependent point-cloud streaming, prioritizing models based on camera view and object visibility to optimize streaming quality.

Contribution

It presents the first mathematical model and heuristic algorithm for rate allocation in multi-point-cloud streaming, enhancing streaming efficiency based on user viewpoint.

Findings

Prioritized bitrate allocation improves visual quality for visible objects.

The heuristic algorithm adapts streaming rates based on camera view and object distance.

First mathematical modeling of rate allocation in point-cloud streaming.

Abstract

In the context of view-dependent point-cloud streaming in a scene, our rate allocation is "adaptive" in the sense that it priorities the point-cloud models depending on the camera view and the visibility of the objects and their distance as described. The algorithm delivers higher bitrate to the point-cloud models which are inside user's viewport, more likely for the user to look at, or are closer to the view camera or, while delivers lower quality level to the point-cloud models outside of a user's immediate viewport or farther away from the camera. For that purpose, we hereby explain the rate allocation problem within the context of multi-point-cloud streaming where multiple point-cloud models are aimed to be streamed to the target device, and propose a rate allocation heuristic algorithm to enable the adaptations within this context. To the best of our knowledge, this is the first work to mathematically model, and propose a rate allocation heuristic algorithm within the context of point-cloud streaming.