In the near future, 5G-connected vehicles will be able to exchange messages with each other, with the roadside infrastructure, with back-end servers, and with the Internet. They will do so with reduced latency, increased reliability, and large throughput under high mobility and user density. Different services with different requirements, such as Advanced Driving Assistance (ADA) and High Definition (HD) Video Streaming, will share the same physical resources, such as the wireless channel. Thus, a rigid orchestration among them becomes necessary to prioritize network resource allocation. This study proposes a Connected Vehicle Service Orchestrator (CVSO) which optimizes the Quality of Experience (QoE) of an in-vehicle infotainment video delivery service, while taking into account the required bandwidth for coexisting high priority services, such as ADA. To this end, we provide an Integer Linear Programming (ILP) formulation for the problem of optimally assigning a video streaming bitrate/quality per user to maximize the overall QoE, considering information from the video service and the Radio Access Network (RAN) levels. Our system takes advantage of recent developments in the area of Multi-access Edge Computing (MEC). In particular, we have implemented the CVSO and other service-level components and have deployed them on top of a standards-compliant MEC platform that we have developed. We exploit MEC-native services such as the Radio Network Information Service (RNIS) to offer the CVSO the necessary level of RAN awareness. Experiments on a full LTE network testbed featuring our MEC platform demonstrate the performance improvements our system brings in terms of video QoE. Furthermore, we propose and evaluate different algorithms to solve the ILP, which exhibit different trade-offs between solution quality and execution time.

在不久的将来，连接5G的车辆将能够与彼此，路边基础设施，后端服务器和Internet交换消息。他们将在高移动性和用户密度下减少等待时间，提高可靠性并提高吞吐量，从而做到这一点。具有不同要求的不同服务（例如高级驾驶辅助（ADA）和高清（HD）视频流）将共享相同的物理资源，例如无线通道。因此，为了优先分配网络资源，它们之间的严格协调成为必要。这项研究提出了一种互联汽车服务协调器（CVSO）它可以优化车载信息娱乐视频交付服务的体验质量（QoE），同时考虑到诸如ADA之类的高优先级服务共存所需的带宽。为此，我们考虑到来自视频服务和无线电接入网（RAN）级别的信息，针对每个用户优化分配视频流比特率/质量以最大化整体QoE的问题，提供了整数线性规划（ILP）公式。我们的系统利用了多访问边缘计算（MEC）领域的最新发展。特别是，我们已经实现了CVSO和其他服务级别组件，并将它们部署在我们开发的符合标准的MEC平台之上。我们利用诸如无线电网络信息服务（RNIS）之类的MEC本地服务为CVSO提供必要的RAN意识级别。在具有我们的MEC平台的完整LTE网络测试平台上进行的实验表明，我们的系统在视频QoE方面带来了性能上的改进。此外，我们提出并评估了解决ILP的不同算法，这些算法在解决方案质量和执行时间之间表现出不同的权衡。