Mobile edge computing (MEC) and field-of-view (FoV) prediction are two key techniques to enable the wireless virtual reality (VR) service. On this basis, we investigate a practical issue, that is, how to efficiently achieve the user's pre-set quality-of-experience (QoE) requirement on both video quality and delay tolerance. A constrained reward-steering algorithm based on reinforcement learning is proposed in this work to solve this multi-objective optimization problem, which finds the optimal policy approaching the user's targeted QoE. Meanwhile, both an instantaneous service delay constraint and a long-term energy constraint are satisfied by the Lagrangian-based method. Simulation results demonstrate that the proposed algorithm outperforms conventional reinforcement learning relying on weights, i.e. achieving an average reward vector much closer to the user's targeted QoE, and meeting both constraints.