Toward Scalable and Efficient Visual Data Transmission in 6G Networks

TL;DR

The paper addresses the growing burden of visual data in 6G networks and surveys a spectrum of approaches to enable scalable, low-latency transmission. It analyzes both conventional and AI-based visual data compression, adaptive streaming, and distributed in-network computing, highlighting their strengths, limitations, and applicability to future networks. Key contributions include detailing end-to-end learned compression techniques with scale hyperprior and attention mechanisms, evaluating ABR and layered coding strategies, and outlining pragmatic properties for scalable deployment: distributed compression, adaptive algorithms, and edge intelligence. The work provides a roadmap for designing 6G-ready visual data pipelines that balance bitrate, latency, and quality across heterogeneous network conditions.

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.