生成式传输：重新思考通信中的计算、带宽和内存
Generative Transmission: Rethinking Computation, Bandwidth, and Memory in Communication
- Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对超低带宽和弱网络条件下视频通信难题，提出生成式传输（GenTrans），基于生成式视频压缩构建，将其作为联合优化问题，利用多种技术降低传输开销，实现高效传输，提升解码效率与鲁棒性，保持感知质量。
AI中文摘要：
在人工智能流框架下，通信正从传输面向保真度的信息流转向跨异构网络资源传递面向任务和感知的令牌流。视频通信是现代信息网络的基本组成部分。然而，在超低带宽和弱网络条件下，主要针对像素级保真度优化的传统视频编码和传输方法，难以平衡视觉可用性、传输效率和对不稳定链路的鲁棒性。随着生成模型的快速发展，视频通信也正从精确信号重建转向接收端感知效用和系统级可用性。本文提出了用于超低带宽和弱网络条件下视频通信的生成式传输（GenTrans）。它基于生成式视频压缩（GVC）构建，将视频传输表述为涉及带宽、计算和内存的联合优化问题，而非仅视为信号编码任务。通过利用生成先验、跨片段内存重用、运行时状态重用和弱网络感知传输，GenTrans显著降低传输开销，同时实现视觉连贯且实用的重建。实验结果表明，GenTrans在超低比特率和弱网络条件下支持有效的视频传输，在保持感知质量的同时提高了传输效率、解码效率和鲁棒性。
英文摘要：
Under the AI Flow framework, communication is shifting from transmitting fidelity-oriented information flows toward delivering task-oriented and perception-oriented token flows across heterogeneous network resources. Video communication is a fundamental component of modern information networks. However, under ultra-low-bandwidth and weak-network conditions, conventional video coding and transmission methods, which are primarily optimized for pixel-level fidelity, often struggle to balance visual usability, transmission efficiency, and robustness to unstable links. With the rapid advancement of generativemodels, video communication is also moving from precise signal reconstruction toward receiver-side perceptual utility and system-level usability. In this paper, we propose Generative Transmission (GenTrans) for video communication under ultra-low-bandwidth and weak-network conditions. Built upon Generative Video Compression (GVC), GenTrans formulates video transmission as a joint optimization problem involving bandwidth, computation, and memory, rather than treating it merely as a signal coding task. By leveraging generative priors, cross-clip memory reuse, runtime state reuse, and weak-network-aware transport, GenTrans significantly reduces transmission overhead while enabling visually coherent and practically useful reconstruction. Experimental results show that GenTrans supports effective video transmission under ultra-low-bitrate and weak-network conditions, achieving improved transmission efficiency, decoding efficiency, and robustness while preserving perceptual quality.
