Development of Predictive TFRC with Neural Network

As Internet real-time multimedia applications increase, the bandwidth available to TCP connections is stifled by UDP traffic, which results in the performance of overall system to be extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example satisfying this necessity. The TCP-Friendly Rate Control (TFRC) is an UDP-based protocol that controls the transmission rate based on the variables such as RTT and PLR. In the conventional data transmission processing, the transmission rate is determined by the RTT and PLR of the previous transmission period. If the one-step ahead predicted values of RTT and PLR are used to determine the transmission rate, the performance of network will be improved significantly. This paper proposes a predictive TFRC protocol with one-step ahead RTT and PLR. A multi-layer perceptron neural network is used as the prediction model, and the Levenberg-Marquardt algorithm is used as a training algorithm. The values of RTT and PLR were collected using UDP protocol in the real system used for NN modeling. The performance of the predictive TFRC was evaluated by the share of Internet bandwidth with various protocols in terms of the packet transmission rate. The extensive experiment of the suggested system in real system was performed and proves its advantages.