2020
DOI: 10.1109/tsipn.2020.2981263
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Incremental Coding for Extractable Compression in the Context of Massive Random Access

Abstract: In this paper, we study the problem of source coding with Massive Random Access (MRA). A set of correlated sources is encoded once for all and stored on a server while a large number of clients access various subsets of these sources. Due to the number of concurrent requests, the server is only able to extract a bitstream from the stored data: no re-encoding can be performed before the transmission of the data requested by the clients.First, we formally define the MRA framework and propose to model the constra… Show more

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Cited by 4 publications
(2 citation statements)
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“…If X and Y are statistically dependent, the coding rate is reduced compared to the case without side information. The Slepian-Wolf setup has regained attention recently, due to its application in modern source coding problems such as Distributed Source coding [2], Multi-View video coding [3], or Massive Random Access to data [4].…”
Section: Introductionmentioning
confidence: 99%
“…If X and Y are statistically dependent, the coding rate is reduced compared to the case without side information. The Slepian-Wolf setup has regained attention recently, due to its application in modern source coding problems such as Distributed Source coding [2], Multi-View video coding [3], or Massive Random Access to data [4].…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, the encoder in IC, relies on the statistics of the side information, and not on its realization, and belongs to the general class of model-based coding problems. Despite the efficiency of some proposed architectures to solve the IC problem [3][4][5], two key questions, related to IC (and thus model-based coding) remain: i) which statistical model should we select and send to the decoder for the data to be compressed? ii) at which encoding rate should we compress the data?…”
Section: Introductionmentioning
confidence: 99%