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Unprecedented bandwidth-intensive, ultra-low latency multi-media services and applications are expected to be delivered through the fifth generation (5G) network. Thus, efficient radio resource management will continue to play an important role as the network service providers strive to provide rich media contents to their users. Therefore, in this work, we consider a cross-layer based framework for efficient allocation of network resources for the transmission of HE VC encoded video streams over 5G-aligned V2X application in mmWave environments. We adopt application and physical layer models, and formulate a multi-vehicle resource allocation problem taking into account the abstracted information of the two corresponding layers. Such a framework is compared with three other standard resource allocation schemes using computed Mean Opinion Score (MOS). Numerical results show that our proposed radio resource management scheme offers a significant improvement of viewer-perceived quality of service compared to the reference approaches.

With the rapid development of mobile communication, human’s demand for communication capacity increasing, a fifth-generation communications network (5G) have gained popularity because of enormous amount of spectrum in the millimeter wave (mmWave) bands. The 5G network is not only to provide people with more high data transfer rates and lower latency, but also to provide users with more meaningful and personalized service based on the users and their understanding of the service required. Nowadays, video streaming service plays an increasing important role in human’s daily life. However, the existing video streaming service application scenarios are mostly in Long Term Evolution network (LTE) and wireless network, there are few articles about studying video streaming service quality evaluation method in 5G network, so we conduct a study on the quality of experience (QoE) of video streaming service under 5G mmWave network scenario with NS-3. Under this scenario, video streaming system is created. By obtaining the quality of service (QoS) parameters of the network scenario, the non-linear regression function is used to predict the QoE of our video streaming service. The fit coefficient of the result shows that our model is powerful.

Quality of experience (QoE) is widely applied to reflect users satisfaction of the network service, which exactly conforms to the user-centric concept in 5G. In this paper, we propose a QoE-based subcarrier and power allocation algorithm for the downlink transmission of a multiuser multiservice system. For the subcarrier allocation algorithm, the rate proportional fairness factor is defined to ensure the fairness between users. Based on different QoE models of three services, i.e., file down (FD), video streaming and voice over internet protocol (VOIP), a multi-objective optimization method is exploited to allocate the power resource by minimizing the total power consumption and maximizing the mean opinion score (MOS) value of users simultaneously. Simulation results indicate that the proposed algorithm has less power consumption and higher QoE performance than the traditional proportional fairness (PF) algorithm. In addition, the proposed algorithm can achieve nearly the same fairness performance as the PF algorithm. Moreover, when the number of subcarriers becomes larger, the power assumption will be less but with little influence on both the QoE and fairness performances.