视频令牌通信：基于UEP的自适应源信道编码的文本意图引导多速率视频令牌通信
Video TokenCom: Textual Intent-Guided Multi-Rate Video Token Communications with UEP-Based Adaptive Source-Channel Coding
AI总结：
本文提出基于UEP的自适应源信道编码的视频令牌通信框架，通过文本意图引导的多速率视频通信提升语义保真度和传输效率。
摘要：
令牌通信（TokenCom）是一种新的范式，受近年来大规模AI模型（LAMs）和多模态大规模语言模型（MLLMs）的成功启发，其中令牌作为统一的通信和计算单元，使未来无线网络中的语义和目标导向的信息交换更高效。本文提出了一种新的视频令牌通信框架，用于文本意图引导的多速率视频通信，采用基于不等误差保护（UEP）的源信道编码自适应技术。所提出的框架整合了用户意图的文本描述与离散视频令牌化和不等误差保护，以在受限带宽条件下提高语义保真度。首先，通过预训练的视频令牌器提取离散视频令牌，同时利用文本条件的视觉语言建模和光流传播来识别跨空间和时间对应于用户意图语义的令牌。其次，我们引入了语义感知的多速率比特分配策略，其中与用户意图高度相关的令牌使用完整的代码书精度编码，而非意图的令牌则通过减少的代码书精度差分编码表示，从而在保持语义质量的同时实现速率节省。最后，开发了一种源和信道编码适应方案，以适应变化的资源和链路条件。在各种视频数据集上的实验表明，所提出的框架在广泛的SNR范围内优于传统和语义通信基线，在感知和语义质量上均表现出色。
英文摘要：
Token Communication (TokenCom) is a new paradigm, motivated by the recent success of Large AI Models (LAMs) and Multimodal Large Language Models (MLLMs), where tokens serve as unified units of communication and computation, enabling efficient semantic- and goal-oriented information exchange in future wireless networks. In this paper, we propose a novel Video TokenCom framework for textual intent-guided multi-rate video communication with Unequal Error Protection (UEP)-based source-channel coding adaptation. The proposed framework integrates user-intended textual descriptions with discrete video tokenization and unequal error protection to enhance semantic fidelity under restrictive bandwidth constraints. First, discrete video tokens are extracted through a pretrained video tokenizer, while text-conditioned vision-language modeling and optical-flow propagation are jointly used to identify tokens that correspond to user-intended semantics across space and time. Next, we introduce a semantic-aware multi-rate bit-allocation strategy, in which tokens highly related to the user intent are encoded using full codebook precision, whereas non-intended tokens are represented through reduced codebook precision differential encoding, enabling rate savings while preserving semantic quality. Finally, a source and channel coding adaptation scheme is developed to adapt bit allocation and channel coding to varying resources and link conditions. Experiments on various video datasets demonstrate that the proposed framework outperforms both conventional and semantic communication baselines, in perceptual and semantic quality on a wide SNR range.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
