Internet content providers often deliver content through bandwidth bottlenecks that are out of their control. Thus, despite often having massively over-provisioned upstream servers, the content providers still cannot control the end-to-end user experience. This paper explores remote traffic shaping, allowing the content provider to allocate its share of a remote bottleneck link across its users using a metric other than TCP fairness, while remaining TCP-friendly to cross traffic on the bottleneck link. To evaluate this approach, we designed FlowTele, the first system that shapes outbound traffic on an Internet-scale network to optimize provider-selected metrics, using source control with neither in-network support nor special client support. Our extensive evaluations over the Internet show that by strategically reallocating bandwidth among provider-owned co-bottlenecked flows, FlowTele improves the provider's total revenue by roughly 20%--30% in various network settings, compared with both (i) status quo TCP fairshare and (ii) recent practice by content providers that proactively throttles video quality during the COVID-19 pandemic, while being TCP-friendly to cross-traffic. Besides revenue, we also study other metrics, such as QoE fairness, that a content provider may wish to optimize using FlowTele.

Data dissemination is a promising application in vehicular ad-hoc networks (VANETs) to overcome the limitation in the connection time of specific vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links, and provide efficient large data file transfer from road-side units (RSUs) to vehicles therein. Unmanned aerial vehicles (UAVs), recently regarded as an effective supplement in wireless networks, can provide line-of-sight (LoS) links with better channel quality, and their high flexibility and maneuverability are beneficial for on-demand deployment in communication systems. In this paper, by employing UAVs as flying relays with data caching capability in VANETs, we design an enhanced UAV-aided data dissemination protocol. Specifically, we propose a centralized UAV trajectory scheduling algorithm based dynamic programming (CTS-DP) to optimize the flying routes of UAVs. Then, based on the scheduled trajectories of UAVs, we further propose a centralized UAV-aided data dissemination scheduling strategy to achieve both effective and efficient coordination of the RSUs, UAVs, and vehicles for data dissemination. Numerical simulations in vehicular scenarios verify the efficiency of the proposed protocol with dynamic UAV trajectory scheduling in terms of downloading progress, data dissemination delay, and system throughput.

In high-speed vehicular ad-hoc networks (VANETs), cooperative data dissemination is an effective solution to amend the limited connection time of communication links between roadside units (RSUs) and vehicles. Existing data dissemination strategies utilize efficient cooperation of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication links to maintain the data transmissions and thus improve the system performance. Recently, unmanned aerial vehicle (UAV) is widely utilized in communication systems, which has a high probability of line-of-sight (LoS) links with better channel quality and can be dynamically deployed. In this paper, we propose a novel UAV-assisted data dissemination scheduling strategy in VANETs. The recursive least squares (RLS) algorithm is utilized to predict the vehicle mobility with low complexity and high prediction accuracy. To enhance the transmission utilities of the UAVs, we further propose a maximum vehicle coverage (MVC) algorithm to schedule the two-dimensional (2D) movements of the UAVs during the process of data dissemination. Simulations in both urban and highway scenarios verify that the proposed UAV-assisted data dissemination strategy achieves a significant reduction of data dissemination delay and an improvement of system throughput.

Multi-hop wireless broadcast is an important component of vehicular networks. Many services rely on the performance of broadcast communication to disseminate data packets. In an urban vehicular network, an efficient way of broadcasting data packets is choosing some vehicles as relay nodes. The base station only needs to multicast data packets to these relay vehicles and the packets would be spread to the whole network through D2D communication among vehicles. In this situation, the strategy of relay selection becomes a key factor of the broadcast efficiency. In this paper, we provide an unsupervised-learning-based method developed from k-means algorithm to help select relay nodes. The base station can learn the distribution of devices itself and choose the relay devices automatically. We use Manhattan map as our map model for the simulation and the result shows great efficiency over a random- selecting strategy.