Neural network prediction model for a real-time data transmission

Both the real-time transmission and the amount of valid transmitted data are important factors in real-time multimedia transmission through the Internet. They are mainly affected by the channel bandwidth, delay time, and packet loss. In this paper, we propose a predictive rate control system for data transmission, which is designed to improve the number of valid transmitted packets for the transmission of real-time multimedia over the Internet. The one-step-ahead round-trip delay time and packet loss are predicted using a prediction algorithm and then these predicted values are used to determine the transmission rate. A real-time multimedia transmission system was implemented using a TCP-friendly algorithm, in order to obtain the measurement data needed for the proposed system. Neural network modeling was performed using the collected data, which consisted of the round-trip time (RTT) delay and packet loss rate (PLR). In addition, the performance of the neural network prediction model was verified through a validation process. The transmission rate was determined from the values of RTT delay and PLR, and a data transmission test for an actual system was performed using this transmission rate. The experiment results show that the algorithm proposed in this study increases the number of valid packets compared with the TCP-friendly algorithm.