This paper introduces MAV-C, an offline, signal-based framework for the joint objective estimation of Audio-Visual Complexity (AVC) in locally-rendered interactive games. MAV-C integrates entropy-based Acoustic Scene Complexity (ASC) features with multi-scale visual complexity metrics adapted to video via optical flow variance, and fuses modality-specific scores via Minkowski pooling. Features are normalized to a common scale relative to analytical bounds, ensuring cross-sequence comparability. We present the framework architecture, report initial verification results on synthetic stimuli with known complexity properties, and outline a parametric sensitivity analysis evaluating the effect of Entropy Weight Method (EWM) regularization, motion scaling, and pooling exponent on discriminability across gameplay sequences of varying complexity.
