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arXiv · 2610.21166

Tokenized Joint Source-Network Coding for Loss-Resilient Video Streaming

Ke Yang·Sixian Wang·Kailin Tan·Guo Lu·Jincheng Dai·Wenjun Zhang·Ping Zhang
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Abstract

For decades, conventional video streaming systems have relied on packet-level forward error correction (FEC) codes and retransmissions to ensure reliable video delivery. However, in real-time communications, FEC is inherently inefficient under unknown and time-varying packet-loss conditions, while retransmission can incur excessive latency and fail to meet stringent playout deadlines. Emerging deep joint source-channel coding (DeepJSCC) provides an alternative by learning channel-noise-resilient representations, yet its tight coupling with the physical layer makes it difficult to integrate into today's layered network architectures and protocols. In this paper, we propose TokJSNC, a simple-but-effective tokenized joint source-network coding paradigm for packet-loss-resilient video streaming. Rather than first compressing video into a compact bitstream and then protecting it with packet-level FEC, TokJSNC directly encodes video into variable-length discrete token-stream, whose transmission rate is jointly adapted to video content complexity and network conditions. By exploiting the spatiotemporal correlations among learned video tokens, TokJSNC enables robust video reconstruction over a wide range of packet-loss rates without explicit FEC or retransmissions. Consequently, TokJSNC brings the JSCC-like graceful degradation property to the network layer while remaining compatible with existing network architectures and transport protocols. Experimental results show that TokJSNC maintains an average playback frame rate above 22 fps even at a 70% packet-loss rate, whereas the best-performing baseline drops below 12 fps. Overall, TokJSNC establishes a practical pathway for repurposing modern generative video tokenizers for loss-resilient streaming, while providing a fundamental framework for video token communications.

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BibTeXRIS

Ke Yang, Sixian Wang, Kailin Tan, Guo Lu, Jincheng Dai, Wenjun Zhang, Ping Zhang. 2026-10-15. Tokenized Joint Source-Network Coding for Loss-Resilient Video Streaming. https://arxiv.org/abs/2610.21166

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