With the rapid development of mobile networks and a great increase in the computing ability of mobile devices, a huge number of people tend to obtain information through mobile networks, which poses some new challenges for real-time on-demand data broadcasting:(1) The data types and sizes are diverse; (2) The real-time characteristics and demand diversity of the user requests greatly increase the volume of hot-spot data (the most access data) and the volume of broadcast data; (3) The users' demands for high service quality become stronger. Current research has been focusing on the fixed-channel models and algorithms and ignoring the changes of real-time data broadcast environments. The problems of fixed-channel models are as follows:(1) They are limited to specific network with fixed channel-models which lack generality; (2) The size and number of channels cannot be adjusted with the changing of broadcast environments automatically. This paper studies the possibility of an automatic channel split and allocation method that can adapt to the environment, and proposes an optimized channel split method (OCSM), which can adjust the bandwidth and number of broadcast channels to the different characteristics of real-time requests. The method includes the following algorithms:(1) A weight average and size cluster algorithm (WASC) for data characteristics mining; (2) A weight evaluating algorithm (R×W/SL) for evaluating the priority of data item; (3) A channel split algorithm (CSA) for channel split. The experiments undertaken in this study include two aspects:(1) Determining the different strategies under different data size distributions and deadline distributions; (2) Verifying the validity of OCSM by validating the effectiveness in different situations through a series of experiments. The results reveal that significantly better performance can be obtained by using the OCSM rather than other state-of-the-art scheduling algorithms.