ODMAO: Online Dependency- and Mobility-Aware Offloading for the Vehicular Edge Metaverse

Vehicular edge metaverse (VEM) applications are multi-stage pipelines whose stages must be placed while vehicles move and links expire. Existing formulations treat tasks as independent, optimize against a static network snapshot, and require future state that no deployed controller can observe. We formulate online VEM offloading as a stochastic, dependency-, mobility-, and cache-aware bi-objective problem and propose Online Dependency- and Mobility-Aware Offloading (ODMAO). ODMAO orders stages by criticality, converts a trajectory forecast with an error model measured on real trajectories into coverage probabilities, admits a remote placement only if its upload and result return are likely to survive, and maximizes expected satisfaction against energy. We evaluate ODMAO against seven controllers on measured drone trajectories of 13,954 vehicles from 33 highD and exiD recordings, tuned and tested on disjoint locations. ODMAO attains the highest priority-weighted satisfaction and completion rate on both datasets. Over the strongest snapshot rule, it gains 0.34 percentage points of satisfaction and 0.78 of completion (1.42 when the cloud is reachable only through roadside units), cuts results lost to vehicle motion from 0.8% to 0.05%, and lies on the satisfaction–energy frontier. An ablation attributes the gain to the trajectory forecast. Coverage, not the controller, bounds safety-class completion.