Scalable On-the-fly Transcoding for Adaptive Streaming of Dynamic Point Clouds

2026-03-09

AI Summary

This work addresses the challenges of high storage overhead and real-time transcoding load in dynamic point cloud streaming under on-demand delivery, which hinder system scalability and user experience. To overcome these limitations, the paper proposes a scalable architecture for adaptive streaming of dynamic point clouds, built upon the V-PCC (Video-based Point Cloud Compression) framework. The design leverages hardware-accelerated video streaming to enable efficient runtime transcoding and integrates a caching mechanism with a predictive transcoding strategy. This approach substantially reduces storage requirements and alleviates real-time transcoding pressure, while maintaining high-quality point cloud rendering. Consequently, it significantly enhances concurrent service capacity and improves perceived quality for end users.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: 3D Computer Vision
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

On-the-fly transcoding of dynamic point cloud sequences reduces storage requirements and virtually increases the number of available representations for on demand streaming scenarios. On-the-fly transcoding introduces, however, additional workload to media providers'infrastructure. While V-PCC encoded content can be efficiently transcoded by re-encoding the underlying video bitstreams, which greatly benefits from hardware-accelerated video codec implementations, the scalability of such a system remains unclear. In this work, we introduce and evaluate a dynamic point cloud streaming system that utilizes on-the-fly transcoding. We explore the limits of scalability of this system in terms of request fulfillment times, specifically evaluating the perceived user Quality of Experience. We empirically show how caching and speculative transcoding allow to significantly reduce transcoding loads, allowing to scale to a higher number of simultaneous clients.

Problem

• dynamic point clouds
• on-the-fly transcoding
• scalability
• adaptive streaming
• Quality of Experience

Innovation

• on-the-fly transcoding
• dynamic point clouds
• scalable streaming
• speculative transcoding
• Quality of Experience