Context Modeling for Efficient Loss-Resilient Neural Video Compression

Rich context modeling gives modern neural video codecs strong rate-distortion performance, while resilience to packet loss has largely been bought by giving that modeling up. We pursue both at once. The obstacle we identify is receiver-side **entropy decoding**. Because the coding distribution is inferred from hyperprior, temporal, and spatial context, an erasure can cause the receiver to use a different distribution, corrupting symbols decoded from intact payloads and propagating damage to later frames. Holding retained payloads byte-identical and correcting only the entropy condition recovers **+5.64 dB at 2% target packet loss**. We formalize the property that excludes this failure, **entropy-condition locality**: the distribution used to decode a retained payload must not depend on what was erased elsewhere. Our codec, **SEAL (Sender-decided Entropy with Assured Locality)**, restores it by serializing the sender's final entropy decision instead of the dependencies that produced it. Sender-only staged refinement preserves rich conditional modeling, while the resulting scale is compressed into **packet-local entropy side information** bound to its residual payload. Both endpoints recover the same coding distribution from this information, without requiring the receiver to reproduce the sender's context. Locality settles interpretation, but the receiver must still reconstruct missing content from a reference state that can diverge from the sender's. **Dual-trajectory training** therefore optimizes reconstruction along a clean sender trajectory and a separate packet-erased receiver trajectory. With side-information cost included, **SEAL** lies on the measured rate-robustness frontier against **GRACE** at its reported rate and the evaluated **H.264/H.265+FEC** configurations under both independent and bursty loss, with gains across **UVG, MCL-JCV, and HEVC** sequences.