Rate-Distortion Performance of Sequential Massive Random Access to Gaussian Sources with Memory

TL;DR

This paper analyzes the rate-distortion performance of sequential massive random access for Gaussian sources with memory, providing achievable storage and transmission rates under distortion constraints for practical applications like Free Viewpoint Television.

Contribution

It introduces a model for SMRA with Gaussian sources with memory, deriving achievable rates considering inter and intra source correlations, which is novel for such correlated source scenarios.

Findings

Derived achievable storage and transmission rates for Gaussian sources with memory.

Analyzed SMRA performance considering inter- and intra-source correlations.

Provided specific examples demonstrating the model's application.

Abstract

In Sequential Massive Random Access (SMRA), a set of correlated sources is jointly encoded and stored on a server, and clients want to access to only a subset of the sources. Since the number of simultaneous clients can be huge, the server is only authorized to extract a bitstream from the stored data: no re-encoding can be performed before the transmission of a request. In this paper, we investigate the SMRA performance of lossy source coding of Gaussian sources with memory. In practical applications such as Free Viewpoint Television, this model permits to take into account not only inter but also intra correlation between sources. For this model, we provide the storage and transmission rates that are achievable for SMRA under some distortion constraint, and we consider two particular examples of Gaussian sources with memory.