StreamAhead: Real-Time Streaming Video Question Answering via Adaptive Precomputation

Haoyang Fan, Dhruv Parikh, Rajgopal Kannan, Viktor Prasanna

Real-time streaming video question answering with vision-language models must answer queries that arrive at any time with low latency while sustaining the video-processing rate required by the application. Existing systems construct key-value (KV) caches for the entire video as it arrives, although a query typically uses only a few of the preceding frames as evidence. This highlights a fundamental trade-off: performing more computation as video arrives reduces query-time computation but can increase processing lag, whereas deferring computation can increase the video-processing rate but requires additional query-time computation for retrieved content. We present StreamAhead, an algorithm-system co-design that exploits this trade-off through adaptive precomputation: dynamically controlling how much computation to complete for each video segment before a query arrives. StreamAhead encodes video segments independently and lets each remain at a different representation level, from inexpensive visual features to costly KV caches, completing KV caches only for the retrieved segments when a query arrives. StreamAhead profiles offline the cost of advancing a segment to each…