# User Preference Aware Lossless Data Compression at the Edge

## TL;DR

This paper introduces a user preference aware lossless data compression method at the network edge, optimizing codebooks based on user preferences to reduce data size and improve efficiency.

## Contribution

It proposes a novel edge source coding approach that incorporates user preferences into lossless data compression, with algorithms for discrete and continuous preferences, and extends to multi-user scenarios.

## Key findings

- Optimal codebook design depends on user preferences.
- Algorithms effectively reduce the expected bits needed for data representation.
- Simulations validate the advantage of preference-aware compression.

## Abstract

Data compression is an efficient technique to save data storage and transmission costs. However, traditional data compression methods always ignore the impact of user preferences on the statistical distributions of symbols transmitted over the links. Notice that the development of big data technologies and popularization of smart devices enable analyses on user preferences based on data collected from personal handsets. This paper presents a user preference aware lossless data compression method, termed edge source coding, to compress data at the network edge. An optimization problem is formulated to minimize the expected number of bits needed to represent a requested content item in edge source coding. For edge source coding under discrete user preferences, DCA (difference of convex functions programming algorithm) based and k-means++ based algorithms are proposed to give codebook designs. For edge source coding under continuous user preferences, a sampling method is applied to give codebook designs. In addition, edge source coding is extended to the two-user case and codebooks are elaborately designed to utilize multicasting opportunities. Both theoretical analysis and simulations demonstrate the optimal codebook design should take into account user preferences.