With the remarkable prosperity in smart consumer electronics (CEs), Mobile Edge Computing (MEC) has emerged as a pivotal technology to tackle the prevalent latency issues faced by smart CEs. Consequently, this has produced multiple deadline-sensitive tasks, imposing stringent computational time requirements on the infrastructure of 6G wireless communication systems. To meet the deadline demands of tasks when offloading, research efforts have focused on improving the scheduling mode. However, a majority of modes are offline, which is unrealistic for MEC servers to schedule tasks or reserve resources according to the global information (i.e., the time and quantity of tasks released by CEs) grasped in advance. To address this concern, we propose an online data-driven scheduling mechanism maximizing the revenue of deadline-sensitive tasks in the MEC-enabled consumer electronics system. Given that the released time and deadline time are fixed, but the execution process are flexible and the execution process involves two network resources, we design a two-step online resource allocation (TORA) algorithm comprising an online spectrum allocation sub-algorithm and an online computing resource allocation sub-algorithm. Moreover, we derive a precise competitive ratio to evaluate the performance of our TORA algorithm. Finally, through extensive simulations, we demonstrate its superiority in improving system revenue.