A combination of biological delays, tumor-microenvironment transport barriers, and dosing cadence determines the success of oncolytic virotherapy. In this study, we develop a delay-differential model incorporating virus-tumor-immune dynamics with explicit infection, lysis, and immune-priming delays, as well as a tunable transport-impedance field that modulates viral contact and diffusion. A tractable viral invasion metric relates barrier severity to eradication thresholds. Meanwhile, stability and Hopf bifurcation analyses demonstrate that cumulative delays can induce oscillations that promote relapse. A safety-aware optimal control formulation, combined with learning-based model predictive control (MPC), enables adaptive, biomarker-guided dosing under toxicity constraints. A spatial reaction-diffusion delay extension reveals stacked traveling fronts under severe transport impedance, which motivates the normalization of transport and optimized therapy sequencing. Based on this framework, personalized oncolytic virotherapy can be designed.