Technological advancements have led to an increase in the number of wireless and mobile devices such as PDAs, laptops and smart phones. This has resulted in an ever- increasing demand for wireless access to the Internet. Hence, wireless mobile traffic is expected to form a significant fraction of Internet traffic in the near future, over the so-called Mobile Internet Protocol (MIP) networks. For real-time applications, such as voice, video and process monitoring and control, deployed over standard IP networks, network resources must be properly allocated so that the mobile end-user is guaranteed a certain Quality of Service (QoS). As with the wired and fixed IP networks, MIP networks do not offer any QoS guarantees. Such networks have been designed for non-real-time applications. In attempts to deploy real-time applications in such networks without requiring major network infrastructure modifications, the end-points must provide some level of QoS guarantees. Such QoS guarantees or QoS control, requires ability of predictive capabilities of the end-to-end flow characteristics. In this research network flow accumulation is used as a measure of end-to-end network congestion. Careful analysis and study of the flow accumulation signal shows that it has long-term dependencies and it is very noisy, thus making it very difficult to predict. Hence, this work predicts the moving average of the flow accumulation signal. Both single-step and multi-step predictors are developed using linear system identification techniques. A multi-step prediction error of up to 17% is achieved for prediction horizon of up to 0.5sec. The main thrust of this research is on the impact of wireless losses on the ability to predict end-to-end flow accumulation. As opposed to wired, congestion related packet losses, the losses occurring in a wireless channel are to a large extent random, making the prediction of flow accumulation more challenging. Flow accumulation prediction studies in this research demonstrate that, if an accurate predictor is employed, the increase in prediction error is up to 170% when wireless loss reaches as high as 15% , as compared to the case of no wireless loss. As the predictor accuracy in the case of no wireless loss deteriorates, the impact of wireless losses on the flow accumulation prediction error decreases.