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

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.