Toward Scalable and Efficient Visual Data Transmission in 6G Networks

TL;DR

This paper reviews techniques for visual data transmission in 6G, emphasizing content compression, adaptive streaming, and distributed in-network computing to address scalability and efficiency challenges.

Contribution

It provides a comprehensive review of current methods and explores the potential of fog-computing architectures for scalable, efficient visual data delivery in 6G networks.

Findings

Content compression and adaptive streaming improve transmission efficiency.

Distributed in-network computing like fog-computing offers scalable solutions.

Technical properties are identified for timely visual data delivery.

Abstract

6G network technology will emerge in a landscape where visual data transmissions dominate global mobile traffic and are expected to grow continuously, driven by the increasing demand for AI-based computer vision applications. This will make already challenging task of visual data transmission even more difficult. In this work, we review effective techniques for visual data transmission, such as content compression and adaptive video streaming, highlighting their advantages and limitations. Further, considering the scalability and cost issues of cloud-based and on-device AI services, we explore distributed in-network computing architecture like fog-computing as a direction of 6G networks, and investigate the necessary technical properties for the timely delivery of visual data.