## 1 Introduction
After a comparison of several viable technologies for the lower layers such as the pros and cons as reviewed in Section 2, we have chosen WiMAX (Worldwide Interoperability for Microwave Access) for its cost-effective approach that can provide a high data rate that can satisfy the needs of our vehicle camera users (low latency and high coverage) at high speed at an affordable cost. It is scalable because its uses OFDMA(Orthogonal Frequency Division Multiple Access) to allow flexible frequency re-use. It implements full MIMO (Multiple-Input and Multiple-Output) which is good for mobile applications by enhancing timely information delivery to save lives and improve quality of life. In view of the complexity problem in implementing an explicit congestion control mechanism in the upper layers to control streaming traffic from driving video in real-time, our present approach is not to \"cure\" congestion but to \"survive\" congestion. We propose to reduce the compression rate of the video packets once congestion is detected in the network. We use the RTCP (Real Time Control Protocol) to feed such information back to the sender. The next step is to find a simulation tool and a realistic vehicular mobility model for integration. Of the few available network simulation languages such as Qualnet and NS-2, we have chosen OPNET because it has different wireless models including WiMAX. It has a rich set of video streaming protocols, and it provides a good framework, which allows us to integrate our mathematical driving video model effectively so that we can explore different scenarios during network traffic congestion. After searching, we have also decided that VanetMobiSim [15, 16] is the only VANET mobility model that would allow the output code generator to be modified in order to generate and output traces useable by other simulators. Consequently, we can integrate its traces into our existing OPNET model using the open-loop approach. To support the experiments from our simulation platform, we need to use realistic driving video traffic streams. Since we do not have an additional ACE [17] license to support the feeding of life data into our OPNET simulator, we shall generate our traffic streams based on a model established from real video traffics transported over wireless networks. Not only do we want to use this model to generate traffic traces quickly, we would also use it to alter the traffic characteristic under different scenarios later on without having to recapture traffic traces from life experiments, which can be both costly and time consuming. The contributions of this paper lie in the following:  
1. The design and implementation of a theoretical but more realistic driving video model than the traditional video model. To the best of our knowledge, there is no comprehensive study related to real-time driving video traffic originated from a vehicle camera, nor any work done on the modeling of the video traffic from the vehicular camera system through the wireless link;
2. The model is a simple model that is a  
2. The integration of a VANET mobility model with such a driving video model to provide a practical VANET simulation platform for the evaluation of video traffic under different scenarios;
3. The design and implementation of a practical traffic controller for driving video over RTP applications. The rest of the paper is organized as follows. Section 2 discusses existing works related to our research, especially their deficiencies. Section 3 discusses the network operations and models used in our testbed. To illustrate the procedure of modeling the driving video traffic in VANET, Section 4 presents an example of the trace collection and stochastic analysis of the trace. We then discuss the OPNET simulation of different scenarios and evaluate their performance in Section 5.