With the proliferation of diverse service characteristics in the internet of vehicles (IoV) under the mobile edge computing (MEC) paradigm, evaluating server-to-end transmission performance presents a significant challenge, particularly due to the complex modeling requirements that must account for service-specific traits in offload feedback strategies. To address this, a cache scheduling evaluation framework is proposed, incorporating time-varying and multi-type services based on queuing theory and a Markov-modulated service process. The proposed framework supports flexible adjustments to service characteristics, bidirectional processing rates, and offload feedback strategies, enabling it to adapt to various communication environments. Within this framework, an offload feedback strategy based on statistical prediction is proposed. Numerical simulations show that the the proposed strategy improves transmission performance by approximately 50% compared with traditional approaches. These findings indicate that the proposed framework provides a valuable reference for designing adaptive strategies under diverse network conditions and hardware configurations.