QoS forecasting for cellular vehicular communications allows cooperative, connected and automated mobility applications to tailor their behavior to the expected communication conditions on the road. In a nutshell, vehicles may, for example, execute cooperative maneuvers if the communication quality of service is only above a certain quantitative level whereas if not they revert to the individual autonomous mode. In this paper, we propose and show empirical methods for estimating packet-based QoS metrics obtained from 5G network measurements with a direct application to vehicular applications. As many distributed vehicular applications possess strict QoS requirements, we focus here on bounding packet-based statistical QoS quantiles, specifically for latency and loss. Our approach is based on training regression neural networks in a federated learning fashion and show that it can obtain predictions on par with centralized training without the vehicles needing to transmit raw measurement data. In contrast to QoS prediction using physical layer information, we briefly discuss the embedding of such much simpler application-level service within the 5G architecture. We also validate our approach through recovering classical closed-form delay quantiles that are obtained from analytical models of simple queueing systems. We show that our approach goes beyond these simple models in that it provides quantile estimates for the complex scenario of cellular vehicle communications and under different application traffic patterns including empirical data traffic traces as well as 5G testbed measurements.

The role of fast yet reliable wireless communications in various application domains is getting ever more important. At the same time, as use cases are becoming more and more complex, application requirements are getting ever more stringent. One example is intelligent transportation, where the efficiency and reliability of wireless data delivery is essential for effective service support. As a consequence, in this context the adoption of AI techniques is widely considered crucial for enabling vehicular communications to adapt to dynamic changes of the environment. In this position paper, we discuss some representative applications of advanced AI tools in vehicular communications. In particular, we elaborate on the potential of distributed learning based on federated learning, of proactive service provisioning, and of graph neural network for enabling AI-native vehicular communications.

This paper presents an overview of the project CASPER, a Marie Curie Research and Innovation Staff Exchange (RISE) project running from 2016 until 2020. The main objective of CASPER is to combine academic and industrial forces towards leveraging the expected benefits of Quality of Experience (QoE) exploitation in future digital networks. In particular, CASPER exploits the most recent advances in communication networks, such as the Software Defined Networking (SDN) and the Network Functions Virtualisation (NFV) in order to design and implement a middleware architecture for QoE-driven service provisioning. CASPER, therefore, addresses the challenges that mobile network operators and service providers face in the current fully digitalized era of communications, concerning the need to provide customer-oriented, reliable, efficient, flexible and dynamic service management.

Over the past years there has been a rapid rise of the mobile communications industry which, combined with the limitations of the current communications networks' structure, necessitates the development of new networks with increased capabilities, so that users can be served with optimal quality of service and network resources utilization. Software Defined Networking (SDN) is a networking approach which transforms the network elements to simple forwarding devices, making decisions centrally. The quality of service perceived by the user, or Quality of Experience (QoE), is considered to be a matter of great importance in software defined networks. This paper presents the SDN QoE Monitoring Framework (SQMF), an SDN application that monitors and preserves the QoE for VoIP and video applications, by periodically monitoring various network parameters and using QoE estimation models.

While video streaming has dominated the Internet traffic, video service providers (VSPs) compete on how to assure the best quality of experience (QoE) to their customers. HTTP Adaptive Streaming (HAS) has become the de facto way that helps VSPs work-around potential network bottlenecks that inevitably cause stallings. However, HAS-alone cannot guarantee a seamless viewing experience, since this highly relies on the mobile network operators' (MNOs) infrastructure and evolving network conditions. Software-defined networking (SDN) has brought new perspectives to this traditional paradigm where VSPs and MNOs are isolated, allowing the latter to open their network for more flexible, service-oriented programmability. This paper takes advantage of recent standardization trends in SDN and proposes a programmable QoE-SDN App, enabling network exposure feedback from MNOs to VSPs towards network-aware video segment selection and caching, in the context of HAS. The video selection problem is formulated using Knapsack optimization and relaxed to partial sub-problems that provide segment encodings that can mitigate stallings. Furthermore, a mobility prediction mechanism based on the Self-similar Least-Action Walk model is introduced, toward proactive segment caching. A number of use cases, enabled by the QoE-SDN App, are designed to evaluate the proposed scheme, revealing QoE benefits for VSPs and bandwidth savings for MNOs.

Video streaming provided by Over-The-Top (OTT) service providers through a cellular network is a common usage scenario for many people today. While video streaming will work reasonably well in a stationary scenario, several issues arise for mobile users. For instance, travelling through short areas without cellular coverage, such as an automobile tunnel, will often result in quality degradation or video stalling. To combat this, this paper provides an analytical investigation of the video quality degradation problem as it is experienced by mobile users and suggests a context-aware HTTP Adaptive Streaming (HAS) strategy to prevent stalling and minimize the impact on Quality of Experience (QoE). This provides a solution that can completely prevent stalling when appropriate context information (such as positional information from satellite navigation) is present. The collected evaluation results encourage further research on how context-awareness can be exploited to further enhance video service provisioning by OTT service providers.

Currently, cellular networks are evolving towards providing high quality mobile services in a ubiquitous manner. In this direction, increased robustness is required to face fluctuations at the service quality that follow traffic demanding events, such as an earthquake or a blackout. Dealing with this challenge, we propose a cross-layer scheme, which handles the traffic changes by adjusting dynamically the encoding rate at the application layer. The rate adjustments target at controlling the impact on the provided Quality of Experience (QoE), i.e., the key metric for assessing the perceptual quality of a provided service. The performance analysis conducted revealed a smooth QoE adaptation to traffic changes with quite low signaling cost for the exchange of the rate adaptation commands.

Device-to-Device (D2D) communications provide the means to users of cellular networks for spatially reusing licensed spectrum by establishing direct connections. Considering the benefits for operators and consumers, a plethora of resource and interference management approaches in the literature deal with parallel cellular and D2D transmissions. However, this feature is not yet supported by modern cellular systems, such as the Long Term Evolution (LTE). The current paper targets at filling this gap by enabling D2D transmissions in LTE networks. Standardized LTE capabilities are utilized to provide a comprehensive framework on which existing or future resource and interference management schemes for D2D communications may be applied. The proposed framework is assessed under a moderate scenario where the cellular resource management policy is not affected. Results show that multiple D2D connections can be established in parallel to cellular uplink transmissions, while a satisfactory percentage of the cell region is available for spatial spectrum reuse.