  Recently, the integration of mobile edge computing (MEC) and generative
artificial intelligence (GAI) technology has given rise to a new area called
mobile edge generation and computing (MEGC), which offers mobile users
heterogeneous services such as task computing and content generation. In this
letter, we investigate the joint communication, computation, and the AIGC
resource allocation problem in an MEGC system. A latency minimization problem
is first formulated to enhance the quality of service for mobile users. Due to
the strong coupling of the optimization variables, we propose a new deep
reinforcement learning-based algorithm to solve it efficiently. Numerical
results demonstrate that the proposed algorithm can achieve lower latency than
two baseline algorithms.
