Frame selection is a fundamental component of multimodal large language models, enabling long videos to be processed under limited visual-token and computational budgets. Uniform sampling preserves temporal coverage but may miss informative content that appears only briefly. To alleviate this limitation, query-dependent methods can retrieve question-relevant frames. However, because the selected frames depend on the current question, the same visual input cannot be directly shared across different questions, and frame selection must be repeated in multi-turn video dialogue. This motivates us to seek a query-independent frame selection method that preserves the reusability of a fixed visual input while improving the coverage of informative events beyond uniform sampling. We propose Multi-Signal Event Modeling and Dynamic Rescoring (MEDR), a training-free and query-independent frame selection method. Multi-Signal Event Modeling organizes complementary visual, motion, and text signals into signal-specific temporal events. Dynamic Rescoring then iteratively reevaluates each candidate relative to the current selected set, updating its score according to frame-level signal strength, additional event coverage, and balanced temporal coverage. The resulting fixed frame set is constructed without observing the query and can be reused across different questions. Experiments on Video-MME and LongVideoBench demonstrate that MEDR achieves competitive performance across two video MLLMs. Under the 64-frame budget, MEDR improves Video-MME accuracy over the strongest evaluated query-independent baseline by 1.33-1.78 percentage points. We further evaluate the reuse of a fixed frame set in multi-question video dialogue.

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