In recent years, compute-intensive and time delay-sensitive applications such as AR/VR, online games, and 4K/8K ultra-high-resolution videos have been emerging. Due to the limitations of their hardware conditions, some mobile devices are unable to calculate such applications under the time-delay requirements, and running such applications will consume huge energy and reduce the endurance of mobile devices. To solve this problem, this study proposes an edge computing offloading and resource allocation scheme in a Wi-Fi network with the coordination of multiple access points (APs). Firstly, the genetic algorithm is utilized to determine the task offloading decision of users. Then, the Hungarian algorithm is used to allocate communication resources to users with task offloading. Finally, according to the time-delay limit of task processing, the computing resources of mobile edge computing (MEC) servers are allocated to the users with task offloading. The simulations reveal that the proposed task offloading and resource allocation scheme can effectively reduce the energy consumption of mobile devices on the premise of meeting the time-delay limit of task processing.