The aim of this chapter is to present different approaches to network traffic management applicable to the IP, transport and application layers in IP, 3G, WiMAX and 4G technologies. The proposed technology for analysis is flexible enough to different types of traffic in opportunistic networks. We start with traffic measurements and obtain accurate data for detail network simulations and precise analysis. Then, we highlight the self-similar nature of the incoming traffic at network nodes. In our next analysis, we look at mapping the measured data with the Polya arrival process by Pareto and gamma distributed inter-arrival times. Polya, Pareto and gamma distributions have the capability to change shape and scale in a way to simulate different types of observed traffic. A proper analytical description of the end-recipient traffic flows and point process of self-similarity inputs are applied for a better user behavior specification. During an end-to-end simulation, more complex queuing models with priorities are proposed. The behavior of the system at its bounds is shown. We map the data from measurements and simulations with the application layer requirements, cross-layer Quality of Service (QoS) and Quality of Experience (QoE) parameters. This is done by traffic fractality analyses, codec-dependent resource reallocation and Fibonacci backward difference traffic moments analyses. All of them demonstrate special moments in the breakdown of the shaping effect. Finally, we express views on openresearch issues for offering optimization in the Internet traffic analyses. Highlights Polya Arrival Process, gamma and Pareto distributions. Description and evaluation of Polya/D/1 model. Numerical results of different peakedness of the traffic input flows. Priority queuing and waiting time limits. Distributed QoS and QoE management. Applicability in Internet of Things and opportunistic networks.