The current trend set by service providers of enhancing the degree of provisions in IP networks while maintaining the optimum quality, has promoted many research activities. These activities include those which aim to introduce improved quality of services for popular Internet services and applications. In this paper, quality of services (QoS) of video transmission on Differentiated Services (DiffServ) with multiprotocol label switching (MPLS) network is being simulated and evaluated by a network Simulator, OPNET. The need of integration of MPLS with DiffServ particularly for the video traffic has been demonstrated in the simulation. The objective of this work is to study the influence of the QoS mechanism viaDiffServ-MPLS on network parameters such as packet loss, delay and throughput for different video resolutions. The comprehensive study showed general improvement in the throughput and packet loss particularly of video transmission when using DiffServ-aware MPLS network as compared to when only MPLS or DiffServ is employed.

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Cognitive radio (CR) is employed as an opportunistic spectrum access which promotes intelligence for 5G wireless networks to provide higher capacity and a network speed of 10Gbps [1]. The need for more capacity will demand more spectrums resulting in integration of CR in 5G networks. The need of CR is to enable much more efficient use of the Abstract: The emerging 5G wireless communications enabled diverse multimedia applications and smart devices in the network. It promises very high mobile traffic data rates, quality of service as in very low latency and improvement in user's perceived quality of experience compared to current 4G wireless network. This encourages the increasing demand of significant bandwidth which results a significant urge of efficient spectrum utilization. In this paper, modelling, performance analysis and optimization of future channel selection for cognitive radio network by jointly exploiting both CR mobility and primary user activity to provide efficient spectrum access is studied. The modelling and prediction method is implemented by using Hidden Markov Model algorithm. The movement of CR in wireless network yields location-varying spectrum opportunities. The current approaches in most literatures which only depend on reactive selection spectrum opportunities result of inefficient channel usages. Moreover, conventional random selection method tends to observe a higher handoff and operation delays in network performance. This inefficiency can cause continuous transmission interruptions leading to the degradation of advance wireless services. This work goal is to improve the performance of CR in terms number of handoffs and operation delays. We perform simulation on our prediction strategy with a commonly used random sensing method with and without location. Through simulations, it is shown that the proposed prediction and learning strategy can obtain significant improvements in number of handoffs and operation delays performance parameters. It is also shown that future CR location is beneficial in increasing mobile CR performance. This study also shows that the number of primary user in the network and the PU protection range affect the performance of mobile CR channel selection for all methods.