MLVC: A Multi-platform Learned Video Codec for Real-World Deployment

Neural video codecs have surpassed classical codecs in codingefficiency but remain impractical for deployment due to cross-platformincompatibility and high computational cost. Existing quantization-basedsolutions fail to produce deterministic results across diverse hardwareplatforms, leading to catastrophic decoding failures. We introduce MLVC,a hardware-robust neural video codec designed for practical cross-platforminference. The key idea is to explicitly transmit scale parameters throughthe hyperprior, which guarantees entropy coding consistency across de-vices without requiring bit-exact arithmetic. While this increases bitrateoverhead, we recover most of the coding efficiency through architecturalimprovements (gated memory, ReGLU activation), a long-term refer-ence recovery mechanism, and domain-specific perceptual training. Onthe VCD video conferencing benchmark, MLVC achieves >70% BD-rate (MOS) improvement over hardware HEVC, the strongest deployablebaseline, while reaching subjective quality competitive with DCVC-RT,which cannot operate across diverse platforms. Both the encoder anddecoder run at 100 FPS on average on commodity NPUs from Apple,Intel, and Qualcomm. MLVC is the first neural video codec to com-bine competitive compression performance, real-time speed, and cross-platform robustness across diverse consumer devices, making it suitablefor widespread deployment. Code will be released.