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

TL;DR

This paper presents a scalable system for on-the-fly transcoding of dynamic point clouds to improve adaptive streaming, demonstrating that caching and speculative transcoding enhance scalability and user experience.

Contribution

It introduces a dynamic point cloud streaming system with on-the-fly transcoding and evaluates its scalability, highlighting the benefits of caching and speculative transcoding.

Findings

Caching reduces transcoding workload.

Speculative transcoding improves request fulfillment times.

System scales to more clients with reduced latency.

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.