Decode-What-Matters: Frame-Level Parallel Generative Decoding to Accelerate Large-Scale Video Analytics.

Video analytics pipelines (VAPs) have been a paradigm for large-scale video analytics. Due to temporal redundancy in video, frame filtering is widely used in VAPs to reduce analysis workload. However, existing works overlook a limitation: while inference operates only on selected frames, decoders must still process many redundant frames due to codec dependencies, leading to over-decoding trap. This limitation stems from the reference-based design in modern codecs, which require decoding preceding frames to reconstruct any selected one. As a result, over-decoding has become the practical bottleneck in VAPs using modern decoders, highlighting a critical but under-explored problem. To address this issue, we propose ParaDeco, a high-throughput video analytics framework featuring a novel frame-level parallel generative decoder. Unlike traditional decoders, ParaDeco adopts a decode-what-matters approach with decoupled frame dependencies. To decode arbitrary frames independently, ParaDeco generates frame-wise features as stand-alone skeletons using compressed video metadata, then predicts pseudo frames maintaining semantic consistency with original frames. Moreover, ParaDeco identifies which frames truly matter for analysis via delicate contribution-based frame filtering. We implement ParaDeco on a cloud server and evaluate it on large-scale real-world video datasets. Our experimental results show that ParaDeco achieves a 2.76x speed-up on average compared to state-of-the-art VAPs.