Mobile Edge Computing (MEC) can effectively speed up data processing and improve Quality of Service (QoS) by offloading Mobile Users' (MUs') tasks to nearby Edge Servers (ESs). However, due to the individual rationality of entities (i.e., ESs and MUs) in MEC networks, they may be reluctant to participate in the computation offloading process without reasonable resource pricing or compensation. To address the challenge, we propose a Two-stage Stackelberg game-based computation Offloading and Resource Pricing mechanism (TORP). Specifically, we first introduce a broker in MEC, which rents computation resources from ESs and provides services to MUs. Next, we formulate the interactions among the broker, MUs, and ESs as a two-stage Stackelberg game, aiming to maximize their respective utilities. Then, we propose a Gradient-Ascent-Based Dynamic Iterative Search Algorithm (GADISA) and an Alternating Iteration-Based Resource Pricing and Task Offloading Algorithm (AIPOA) to solve the optimization problem. Finally, simulations show that TORP greatly outperforms other benchmarks in improving the utilities of three entities.