Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute-and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include latency minimization, energy conservation, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions.

Immersive media services, such as augmented and virtual reality (AR/VR)a, 360-degree video, and freeviewpoint video (FVV), are popular today. They require massive data storage, ultra-high computing power, and ultra-low latency. It is hard to fulfill these requirements simultaneously in a conventional communication system using a cloud/centralized radio access network (C-RAN). Specifically, due to centralized processing in such a system, the end-to-end latency as well as the burden on the fronthaul network are expected to be high. Fog computing-based radio access networks (F-RAN), in contrast, have been widely considered as an enabler for immersive media. Our contribution in this paper is threefold: First, we propose various service scenarios reflecting the characteristics of immersive media. Second, we identify the technologies that are required to support the proposed service scenarios under F-RAN, and discuss how they can support the proposed scenarios efficiently. Third, we discuss possible research opportunities. a A list of acronyms can be found in Appendix A.

Sparse Random Linear Network Coding (RLNC) reduces the computational complexity of the RLNC decoding through a low density of the non-zero coding coefficients, which can be achieved through sending uncoded (systematic) packets. However, conventional recoding of sparse RLNC coded packets at an intermediate node in a multi-hop network increases the density of non-zero coding coefficients. We develop and evaluate sparsity-preserving recoding (SpaRec) strategies that preserve the low density of non-zero coding coefficients of sparse RLNC with systematic packets. We develop SpaRec strategies with and without decoding at the intermediate nodes, with and without a specified coding rate, as well as with finite and infinite recoding window lengths. We evaluate the SpaRec strategies in multi-hop networks in terms of packet loss, packet delivery delay, as well as recoding and decoding (computation) throughput. We find that the SpaRec strategies substantially improve the RLNC performance compared to conventional recoding.

In an era of rapidly evolving technology, the pursuit of higher data rates, extremely low latency, and robust reliability is becoming increasingly crucial. Applications such as Industrial Internet of Things (IIoT) demand that transmitted messages meet stringent time constraints. Traditional ARQ-based schemes struggle to achieve the required performance in the presence of feedback delays or feedback losses. Unlike conventional ARQ approaches, which acknowledge only original packets, network coding can acknowledge multiple degrees of freedom (DOF), making the feedback mechanism a pivotal factor for reliability-delay tradeoffs. On the other hand, UDP protocol operates blindly without feedback acknowledgments (ACKs). Thus, the selection of code to provide ultra-reliable and low-latency communications (URLLCs) over lossy channels without feedback ACKs is tedious. However, network coding inherent robustness against packet erasure positions it as a promising candidate for reliable communication without feedback ACKs. In this survey, we delve into cutting-edge opportunistic network coding schemes that can deliver high data rates and low latency, even in the face of feedback limitations such as feedback delays, feedback losses, or complete feedback absence. We focus on how network coding-based approaches maintain effective communication over lossy networks, regardless of these feedback limitations. In the context of UDP environments (i.e., blind coding), we present the interplay of some essential elements that must be integrated into network coding to enable reliable transmission without feedback ACKs. Our expectation is that this paper will set the stage and raise the interest of readers willing to explore Network codings (NCs) in the direction of URLLC for mission-critical (IIoT) applications.

This paper addresses a novel virtual reality (VR) system that is based on the real world in which we live. The ultimate goal is to implement it as though a VR user freely exists in a place. To this end, it is most important to reconstruct a VR space that provides six degree-of-freedom (DOF), namely, yaw, pitch, roll, surge, sway, and heave. However, most currently released VR services that are based on the real world limit users’ movements to three DOF. Even if the services support six DOF, most are highly complex and based on computer graphics. To overcome this problem, we first assume that there is a full Internet of things (IoT) infrastructure for collecting important data for VR space reconstruction. This assumption is realistic because many researchers expect that in the near future, IoT technology will lead to a world that connects not only people to people but also things to things. In this paper, we propose an end-to-end system architecture for the VR space that is based on the real world along with the element technologies that constitute the proposed system. This paper also includes a detailed survey of both conventional and emerging studies by other researchers.

Emerging microservices demand flexible low-latency processing of network functions in virtualized environments, e.g., as containerized network functions (CNFs). While ensuring highly responsive low-latency CNF processing, the computing environments should conserve energy to reduce costs. In this systems integration study, we develop and evaluate the novel XDP-Monitoring Energy-Adaptive Network Functions (X-MAN) framework for managing the CPU operational states (P-states) so as to reduce the power consumption while prioritizing low-latency service. Architecturally, X-MAN consists of lightweight traffic monitors that are attached to the virtual network interfaces in the kernel space for per-CNF traffic monitoring and a power manager in user space with a global view of the CNFs on a CPU core. Algorithmically, X-MAN monitors the CPU core utilization via hybrid simple and weighted moving average prediction fed by the traffic monitors and a power management based on step-based CPU core frequency (P-state) adjustments. We evaluate X-MAN through extensive measurements in a real physical testbed operating at up to 10 Gbps. We find that X-MAN incurs significantly shorter and more consistent monitoring latencies for the CPU utilization than a state-of-the-art CPU hardware counter approach. Also, X-MAN achieves more responsive CPU core frequency adjustments and more pronounced reductions of the CPU power consumption than a state-of-the-art code instrumentation approach. We make the X-MAN source code publicly available.