The conventional network infrastructure is struggling to keep up with the rapidly growing demands of modern society. The explosion of data, the increasing number of connected devices, and the growing reliance on real-time applications are all putting pressure on the current network which almost reaches Shannon’s limit. In this paper, we propose Meta-Networking, an advanced networking architecture that can provide beyond Shannon communications by utilizing multi-faceted information from different domains, based on an intelligent collaboration among distributed network entities. An overview of Meta- Networking is provided and the key principles and components of Meta-Networking, including the quality-of-experience characterization, AI-empowered semantic encoding, and information density improvement, are analyzed. It enables a groundbreaking communication system where a much larger amount of information is transmitted without increasing the size of binary digits. Furthermore, an application scenario for image transmission in the Internet of Vehicles (IoV) is discussed, which shows a significant performance improvement as compared with conventional communications. It is believed that Meta- Networking has the potential for revolutionizing communication systems with higher efficiency, stronger reliability, and intelligence awareness.

In recent years, automatic mobile robots (AMR) have been widely concerned in various fields, such as material handling, unmanned aerial vehicle (UAV) cruise, automatic factories and so on. Compared with traditional robots, an AMR bring system poses several challenges such as adaptation to the environment, stable communication and robust control. For example, when in a complex environment, it can observe the surrounding environment in real time to avoid obstacles, and track the target in real time to change its trajectory until the task is completed, which improves the flexibility and mobility of the robot. More importantly, AMR systems require supports from various technologies including real-time positioning and navigation, trajectory planning, target tracking, multi-agent systems, robot monitoring and so on. In the near future, its geometric adaptability, design characteristics, mobility, flexibility, obstacle avoidance and fault-tolerant control will be further improved. This article gives a comprehensive elaboration on the four aspects of AMR systems, namely, AMR applications, perception technologies, robot communications, and control mechanisms. Future research directions are also pointed out.

Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.

Volumetric video (or hologram video), the medium for representing natural content in VR/AR/MR, is presumably the next generation of video technology and a typical use case for 5G and beyond wireless communications. To realize volumetric video applications, efficient volumetric video streaming is in critical demand. This article responds to the challenges of and propose solutions to wireless transmission systems of point cloud video, which is the most popular and favored way to represent volumetric media and significantly differs from the other types of videos. In particular, we first introduce point cloud video technology and its applications, and then discuss the challenges of and solutions to point cloud video streaming, including encoding, tiling, viewing angle prediction, decoding, quality assessment and transmission optimization. Furthermore, we explain a prototype of MPEG DASH-based point cloud video streaming system as a preliminary study, along with more simulation results to verify its performance. Finally, we identify future research directions for providing high-quality point cloud video streaming.

Point cloud video provides 6 degrees of freedom (6DoF) viewing experiences to allow users to freely select the viewing angles of 3D scenes and is expected to be the next-generation video. This paper studies the point cloud video streaming and proposes a fuzzy logic-based point cloud video streaming scheme to solve the inherent technical issues. In particular, a point cloud video is first partitioned into smaller tiles, along with a low-quality base layer covering the entire video. Each tile is encoded into different quality levels, and both the compressed and uncompressed (i.e., decoded) versions of each tile are prepared for selection. Then, based on the user’s viewing angle and predicted future network bandwidth condition, fuzzy logic empowered quality level selection, with properly defined novel fuzzification, fuzzy rules, and defuzzification, is conducted to maximize the received point cloud video quality under the communication resource, computational resource and quality requirements constraints. Extensive simulations based on real point cloud video sequences and network traces are conducted, and the results reveal the superiority of the proposed scheme over the baseline scheme. To the best of our knowledge, this is the first work studying point cloud video streaming using fuzzy logic.

Some vehicular Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative learning-based routing scheme for multi-access vehicular edge computing environment. The proposed scheme employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead. The routes are also preemptively changed based on the learned information. By integrating the “proactive’‘ and “preemptive” approach, the proposed scheme can achieve a better forwarding of packets as compared with existing alternatives. We conduct extensive and realistic computer simulations to show the performance advantage of the proposed scheme over existing baselines.