The proliferation of delay-sensitive applications in the Internet of Vehicles (IoV) poses significant challenges to conventional vehicular edge computing (VEC), particularly in terms of limited coverage and constrained computing resources. To address these issues, this paper proposes a market-driven air–ground collaborative vehicular edge computing framework that unifies heterogeneous computing services provided by an unmanned aerial vehicle (UAV), roadside unit (RSU), and vehicle platoon (VP) within a common pricing-and-allocation mechanism. The interaction among service providers and the user vehicle (UV) is modeled as a multi-leader single-follower Stackelberg game, where UAV, RSU, and VP act as heterogeneous leaders that announce service prices, and the UV acts as the follower that determines task allocation ratios. The main contribution of this work lies in establishing a unified economic coordination model for heterogeneous air–ground edge services, together with a numerical equilibrium computation framework tailored to the resulting low-dimensional bounded pricing game. We show that the follower-side optimization problem is convex and admits a unique optimal response, and that the upper-level pricing game admits at least one Nash equilibrium. Based on this structure, we develop a coarse-to-fine grid-search-based Stackelberg equilibrium computation method (CFGS-SE), which directly verifies best-response consistency through numerical evaluation and local refinement. Simulation results show that the proposed method achieves high follower utility and low energy consumption while maintaining competitive delay performance and balanced task allocation. These results demonstrate that the proposed framework provides an effective solution for price-guided task allocation in air–ground collaborative vehicular edge computing.

The Domain Name System (DNS) serves as a foundational networking service, yet its inherent time-to-live (TTL)-based cache mechanism presents a conundrum—striving for both low query latency and robust cache consistency proves challenging. To address this, we introduce Aeacus: a middleware seamlessly integrated into the 5G core, engineered to optimize name resolution for QUIC. Aeacus adeptly fortifies DNS with substantial cache consistency by capitalizing on QUIC handshake states to detect cache inconsistency, without compromising query delay. Furthermore, Aeacus orchestrates the amalgamation of DNS queries and QUIC handshake messages, effectively truncating one round-trip of message exchange and reviving expired DNS cache to bolster cache hit rates. Our dual-pronged deployment, encompassing both commercial and test 5G networks, demonstrates Aeacus’ prowess. In direct comparison with DNS, Aeacus successfully truncates connection setup delays by a remarkable 8.9% to 71.8%, all while introducing a mere 5.9% overhead attributed to supplementary packet processing and forwarding expenses. Importantly, existing DNS-based systems reap the benefits of Aeacus without necessitating modifications. We demonstrate Aeacus’ seamless enhancement of DNS-based load balancers, extending QUIC's 0-RTT handshake to include 0-RTT connection setup and service migration.

Deep Reinforcement Learning (DRL) has rapidly gained traction as a viable method for optimizing control in real-time video streaming. Recent research endeavors are shifting towards enabling direct DRL control over multiple streaming parameters, moving away from traditional bitrate only control. Despite this growing interest, there is a notable absence of a dedicated open-source framework to facilitate such research.

The rapid proliferation of learning systems in an arbitrarily changing environment mandates the need to manage tensions between exploration and exploitation. This work proposes a quantum-inspired bandit learning approach for the learning-and-adapting-based offloading problem where a client observes and learns the costs of each task offloaded to the candidate resource providers, e.g., fog nodes. In this approach, a new action update strategy and novel probabilistic action selection are adopted, provoked by the amplitude amplification and collapse postulate in quantum computation theory. We devise a locally linear mapping between a quantum-mechanical phase in a quantum domain, e.g., Grover-type search algorithm, and a distilled probability-magnitude in a value-based decision-making domain, e.g., adversarial multi-armed bandit algorithm. The proposed algorithm is generalized, via the devised mapping, for better learning weight adjustments on favorable/unfavorable actions, and its effectiveness is verified via simulation.

Real-time deep video analytic at the edge is an enabling technology for emerging applications, such as vulnerable road user detection for autonomous driving, which requires highly accurate results of model inference within a low latency. In this paper, we investigate the accuracy-latency trade-off in the design and implementation of real-time deep video analytic at the edge. Without loss of generality, we select the widely used YOLO-based object detection and WebRTC-based video streaming for case study. Here, the latency consists of both networking latency caused by video streaming and the processing latency for video encoding/decoding and model inference. We conduct extensive measurements to figure out how the dynamically changing settings of video streaming affect the achieved latency, the quality of video, and further the accuracy of model inference. Based on the findings, we propose a mechanism for adapting video streaming settings (i.e. bitrate, resolution) online to optimize the accuracy of video analytic within latency constraints. The mechanism has proved, through a simulated setup, to be efficient in searching the optimal settings.

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities [a.k.a fog nodes (FNs)]. In other words, VFC proposes to complement stationary FNs co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available FNs. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatiotemporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, and the mobility of FNs. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile FNs to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatiotemporal variation in both demand and supply. Using real-world traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile FNs and create more savings in operational costs in the long term.

The business potential of device-to-device (D2D) communication including public safety and vehicular communications will be realized only if direct communication between devices subscribed to different mobile operators (OPs) is supported. One possible way to implement inter-operator D2D communication may use the licensed spectrum of the OPs, i.e., OPs agree to share spectrum in a co-primary manner, and inter-operator D2D communication is allocated over spectral resources contributed from both parties. In this paper, we consider a spectrum sharing scenario, where a number of OPs construct a spectrum pool dedicated to support inter-operator D2D communication. OPs negotiate in the form of a non-cooperative game about how much spectrum each OP contributes to the spectrum pool. OPs submit proposals to each other in parallel until a consensus is reached. When every OP has a concave utility function on the box-constrained region, we identify the conditions guaranteeing the existence of a unique equilibrium point. We show that the iterative algorithm based on the OP's best response might not converge to the equilibrium point due to myopically overreacting to the response of the other OPs, while the Jacobi-play strategy update algorithm can converge with an appropriate selection of update parameter. Using the Jacobi-play update algorithm, we illustrate that asymmetric OPs contribute an unequal amount of resources to the spectrum pool; however, all participating OPs may experience significant performance gains compared with the scheme without spectrum sharing.

In order to harvest the business potential of deviceto-device (D2D) communication, direct communication between devices subscribed to different mobile operators should be supported. This would also support meeting requirements resulting from D2D relevant scenarios, like vehicle-to-vehicle communication. In this paper, we propose to allocate the multi-operator D2D communication over dedicated cellular spectral resources contributed from both operators. Ideally, the operators should negotiate about the amount of spectrum to contribute, without revealing proprietary information to each other and/or to other parties. One possible way to do that is to use the sequence of operators' best responses, i.e., the operators make offers about the amount of spectrum to contribute using a sequential updating procedure until reaching consensus. Besides spectrum allocation, we need a mode selection scheme for the multi-operator D2D users. We use a stochastic geometry framework to capture the impact of mode selection on the distribution of D2D users and assess the performance of the best response iteration algorithm. With the performance metrics considered in the paper, we show that the best response iteration has a unique Nash equilibrium that can be reached from any initial strategy. In general, asymmetric operators would contribute unequal amounts of spectrum for multi-operator D2D communication. Provided that the multi-operator D2D density is not negligible, we show that both operators may experience significant performance gains as compared to the scheme without spectrum sharing.