Abstract
Coordinating mixed fleets of massive vehicles under stringent delay constraints is a central scalability bottleneck in next-generation mobile computing networks, especially when passenger cars, freight trucks, and autonomous vehicles share the same radio and multi-access edge computing (MEC) infrastructure. Heterogeneous mean field games (HMFG) are a principled framework for this setting, but a fundamental design question remains open: how many agent types should be used for a fleet of sizeN ? The difficulty is a two-sided trade-off that existing theory does not resolve: using more types improves heterogeneity representation, but it reduces per-class sample size and weakens the mean-field approximation accuracy. This paper resolves that trade-off through an explicitε -Nash error decomposition, a closed-form type-selection law, a heterogeneity-aware equilibrium solver, and a robust extension to time-varying LEO backhaul dynamics. For the 1D queue state space, the optimal type count satisfiesK^(*)(N)=Θ(N^(1/3)) ; for the joint queue-channel model ( d=2 ), the scaling becomesK^(*)(N)=Θ(N^(1/5))with logarithmic correction. The unified formulaK^(*)(N)=Θ(N^(α/(α+β)))provides dimension-dependent design guidance, reducing type granularity to a principled, set-once system parameter rather than a per-deployment tuning burden. Experiments validate the 1D scaling law with empirical slope0.334 ± 0.004 , achieve2.3×faster PDHG convergence atK=5 , and deliver up to29.5%lower delay and60%higher throughput than homogeneous baselines. Unlike model-free DRL methods whose training complexity scales with the state-action space, the proposed HMFG solver has per-iteration complexityO(K² N_(q) N_(t))independent of fleet sizeN , making it suitable for large-scale mobile edge computing deployment.