Tube-Structured Incremental Semantic HARQ for Generative Video Receivers
For bandwidth-limited video delivery with generative receivers, this work introduces a novel retransmission primitive that improves error resilience under budget constraints, though gains are incremental and diminish in clean channels.
This paper proposes a receiver-driven semantic HARQ scheme for generative video reconstruction that uses tube-structured package-native retransmission primitives, achieving lower time-weighted recovery cost than block-based baselines in moderate-to-harsh channel conditions, with gains mainly from earlier stabilization of recovery trajectories.
Generative semantic communication uses receiver-side generative priors to reconstruct visual content from compact semantics, making it attractive for bandwidth-limited multimedia delivery. For video, reliable recovery remains difficult because errors accumulate over time, useful evidence is temporally correlated, and the receiver must make decisions under limited interaction, retransmission, and reconstruction budgets. Existing generative semantic communication studies mainly emphasize representation, compression, or generative reconstruction, while recent error-resilient and semantic-HARQ methods still largely operate on encoder-defined or frame-block retransmission units. This paper studies receiver-driven semantic HARQ for generative video reconstruction under a budget-constrained AoIS-AUC objective and argues that the retransmission primitive is itself an important system design variable. We propose tube-structured package-native requests, in which temporally local packages are the channel-visible HARQ objects and are transmitted, dropped, received, and committed at package granularity. Under a controlled comparison protocol with matched backbone, budgets, and channel model, this primitive yields lower time-weighted recovery cost than competitive block-based baselines in practically relevant moderate-to-harsh regimes, while the gap naturally shrinks in near-clean channels. The gain mainly appears as earlier stabilization of the recovery trajectory, while final-quality endpoints remain broadly comparable, and it persists even against a tube-aware block-ranking baseline.