Transmission and Storage Rates for Sequential Massive Random Access

TL;DR

This paper proposes a new source coding framework called SMRA that efficiently handles massive, sequential client requests for correlated data sources with minimal re-encoding, optimizing storage and transmission rates.

Contribution

It formally defines the SMRA paradigm, derives theoretical bounds for various source types, and demonstrates practical implementations with LDPC codes, advancing data transmission efficiency.

Findings

SMRA achieves point-to-point transmission rates with manageable storage overhead.

Theoretical bounds are established for lossless and lossy sources.

Practical LDPC-based systems validate the approach.

Abstract

This paper introduces a new source coding paradigm called Sequential Massive Random Access (SMRA). In SMRA, a set of correlated sources is encoded once for all and stored on a server, and clients want to successively access to only a subset of the sources. Since the number of simultaneous clients can be huge, the server is only allowed to extract a bitstream from the stored data: no re-encoding can be performed before the transmission of the specific client's request. In this paper, we formally define the SMRA framework and introduce both storage and transmission rates to characterize the performance of SMRA. We derive achievable transmission and storage rates for lossless source coding of i.i.d. and non i.i.d. sources, and transmission and storage rates-distortion regions for Gaussian sources. We also show two practical implementations of SMRA systems based on rate-compatible LDPC codes. Both theoretical and experimental results demonstrate that SMRA systems can reach the same transmission rates as in traditional point to point source coding schemes, while having a reasonable overhead in terms of storage rate. These results constitute a breakthrough for many recent data transmission applications in which different parts of the data are requested by the clients.