In this paper, we propose a resilient and fast block transmission system for Hyperledger Fabric in multi-cloud environments. The goal of the proposed system is to improve the scalability, transaction throughput, and resilience of Hyperledger Fabric by minimizing the block synchronization time among nodes. To achieve this goal, the proposed system is designed to deliver blocks quickly and reliably to all the participating nodes in time-varying multi-cloud environments. The proposed system includes the delay estimating process with O(N) control message overhead over the P2P network, the effective bandwidth estimating process for block transmission, the Gaussian Mixture Model-based clustering and cluster leader selecting process, and hybrid P2P multicast tree constructing process. In addition, a control message format and delivery process are proposed to efficiently provide hybrid P2P multicast tree and neighbor nodes information to all the participating nodes. And we propose a pull-based local block loss recovery process that can receive lost blocks from multi-node without complicated scheduling using a rateless code. The proposed system is fully implemented by using well-known open sources (e.g., Hyperledger Fabric, Docker, Containernet, and Mininet) and Go/C/Python. Experiment results show that the proposed system can reduce the maximum block arriving time among all the participating nodes by approximately 50%~95% compared to the existing algorithms. This improves not only blockchain transaction per second, but also resilience to various network-layer vulnerabilities and attacks that may occur when the block propagation delay increases.

This paper presents a novel HTTP adaptive augmented reality (AR) streaming system. The goal of the proposed system is to provide high quality AR streaming services with low latency through a time-varying wireless network. In the proposed system, we employ a progressive mesh technique to enhance the scalability of AR streaming services, and adopt the metafile structure to support various attributes of progressive meshes necessary for rendering, displaying, and scheduling according to wireless network conditions. Based on this metafile, the proposed system requests the next chunk among AR contents to improve human visual perceptual quality considering the displaying scale values of AR contents on the screen. The proposed system is fully implemented by using well-known open sources (e.g., Android, OpenCV, OpenGL, and Nexus) and C/C++. Finally, the system is examined in real wireless network environments.

This work presents a novel HTTP adaptive streaming system to maximize overall video streaming service quality of all the clients over SDN-enabled Wi-Fi APs. To archive the goal, the proposed system employs multipath technology to overcome the limitation of a Wi-Fi AP, and SDN technology to effectively harmonize multiple Wi-Fi APs. During the streaming services, SDN controller periodically monitors the traffic information collected from the APs, and then determines video bitrate and segment transmission schedule based on this information. The proposed system is fully implemented in real Wi-Fi network environments.

This paper presents a quick block transport system for scalable Hyperledger Fabric (HLF) blockchain over device-to-device (D2D)-assisted 5G networks. The objective of the proposed system is to increase the transaction throughput and further enable the application of the blockchain to delay-sensitive 5G services. To this end, the system rapidly disseminates the block data to all the peers on the blockchain network. The blockchain network is organized with mobile edge cloud (MEC) server and mobile peers in the 5G network. To ensure a scalable and reliable blockchain architecture, the core functionalities of the HLF blockchain are appropriately distributed among the mobile edge cloud and each mobile peer. The MEC server handles core functionalities for the blockchain network, such as block generation and membership management. In addition, it is responsible for gathering the network status of all peers and organizing the overlay structure to be utilized to rapidly propagate the block with a low network cost. On the other hand, the mobile peers handle the endorsing and validating functionalities to prevent block data tampering in the blockchain network. Furthermore, locality-aware device-to-device networking is utilized in 5G network coverage to achieve faster block dissemination. The proposed system is designed to work seamlessly in an existing HLF blockchain framework with consideration of scalability and compatibility. It is fully implemented using the open-source HLF and OpenAirInterface. To verify the effectiveness of the proposed block transport system, it was evaluated in an actual 5G network as well as 802.11 network. The experimental results verified its feasibility.

This work presents a progressive mesh-based HTTP adaptive augmented reality (AR) streaming system that provides high quality AR streaming services with low latency over a time-varying wireless network. In the proposed system, a progressive mesh is employed to support the scalability of AR streaming services, a novel meta-file is proposed to efficiently provide the rate and view-dependent quality information. By using this meta-file, the proposed system determines the next request chunk to enhance human visual perceptual quality considering the view-dependent projected images of AR objects on the screen. The proposed system is realized using open sources (e.g., Android, OpenCV, OpenGL, and Nexus). Finally, experimental results are given to verify the performance improvement of the proposed system.

This work presents a cloud-assisted augmented reality (AR) streaming service system over wireless network. The goal of the proposed system is to provide interactive AR streaming services of high quality with low latency through a time-varying wireless network. In the proposed system, we employ progressive mesh technique to enhance the scalability of AR streaming services, and adopt meta-file structure to include various attributes of progressive meshes of AR contents necessary for rendering and displaying according to the wireless network conditions. Based on this meta-file provided by a cloud AR server, the proposed system requests the next chunk among AR contents to improve the human visual perceptual quality of all selected AR contents while considering the marker sizes detected by the camera of the mobile AR device. The proposed system is fully implemented by using well-known open sources (e.g., Android, ARCore, and Nexus) and C/C++. Finally, the system is examined in real wireless network environments.

Summary This paper proposes a novel congestion‐aware HTTP adaptive streaming (HAS) system to improve video quality of clients over software‐defined network (SDN)–enabled Wi‐Fi network. The proposed system employs SDN technology to efficiently control the network resources and video quality over the competing Wi‐Fi network. In the proposed system, an SDN‐enabled access point (AP) can monitor the streaming status of clients, such as the buffered playback time and information of requested segments. An SDN controller collects not only the streaming status of clients but also the Wi‐Fi network conditions from the AP using OpenFlow. Additionally, when a congestion is going to occur, it determines the segment bitrate of each client to provide seamless and high‐quality streaming services. The proposed system is fully implemented using open‐source programs, such as ONOS and hostapd, and is examined in a real Wi‐Fi network. The experimental results verify that the proposed system can provide seamless and high‐quality HAS services for the clients compared with existing systems.

This work presents a cooperative and distributive caching system over the information-centric networking (ICN) tree. The goal of the proposed caching system is to provide seamless video streaming services with high network utilization over the ICN tree. The proposed system employs a metafile made by a scalable caching scheme for simple but effective Content Store (CS) management, and the caching information among ICN nodes is delivered by modified ICN interest/data packet headers with no additional control overhead. Based on the delivered caching information and the metafile, each ICN node determines its own caching chunks in a distributive way to minimize the required peak bandwidth for seamless streaming. The proposed caching system is fully implemented using the NDNSim for large scale simulation. Moreover, a small-scale real cellular wireless network testbed is realized by using CCNx and Raspberry PIs.

In this work, we propose an energy-efficient HTTP adaptive streaming with hybrid TCP/UDP over heterogeneous wireless networks. Unlike the existing HTTP adaptive streaming system, the proposed system can utilize both TCP and UDP to overcome the performance degradation problem of TCP and improve the energy efficiency. The proposed system handles the types of transport protocols, requested video quality, and amount of data received through each wireless network as control variables based on network conditions, buffer occupancy, and energy condition of mobile terminal. When UDP is selected, the proposed system employs Raptor code as a forward error correction scheme to support a reliable data delivery, and adaptively chooses the proper Raptor code parameters such as symbol size and amount of redundant data, and requests redundant data from the server to reduce the overhead. The proposed system is fully implemented in an Android-based mobile terminal and tested in real WiFi and LTE networks. The experiment results show that the proposed system can achieve good energy efficiency while providing a seamless high-quality HTTP adaptive streaming service.

This work presents a video quality-aware traffic steering system that provides stable video streaming services to users by steering part of the macrocell traffic into small cells. The proposed system achieves a good balance between fairness and social welfare in terms of the video quality by allocating the radio resource of the macro base station. The user data flow is split into two parts toward the macro base station and small cell AP, and the users simultaneously receive their data from both. In the proposed system, fountain code is employed to achieve dual connectivity enhancement by overcoming practical issues in the dual connectivity architecture. Furthermore, SDN technology is adopted not only to quickly react to time-varying network status but also to control network resources efficiently. The proposed resource allocation process is designed to require a low running time complexity. The proposed system is implemented using C/C++, Java, and a well-known ONOS open source. The experimental results verify that the proposed system can achieve much better performance than other traffic steering algorithms.