In this paper, we present a watchdog blockchain network (WDN) that allows for simultaneous scalability and decentralization enhancements. Because of the blockchain trilemma, achieving high levels of security, scalability, and decentralization simultaneously is challenging. Typically, public blockchains are fully decentralized at the expense of scalability, whereas private blockchains achieve high scalability at the expense of decentralization. A WDN is proposed to compensate for the low-level decentralization of a private blockchain while maintaining its high-level scalability. A WDN may prevent arbitrary forgery, alteration, and deletion of distributed ledgers in private blockchains owing to weak decentralization. However, the transaction throughput gap between the WDN and the private blockchain can be a significant obstacle in operating a WDN with a private blockchain. Therefore, the WDN in this study has been designed to back up data on the private blockchain in real time over a distributed network environment. The WDN features a network-aware consensus protocol that can quickly complete the consensus process of the three phases. To this end, modern network technologies such as GossipSub, quick UDP internet connections (QUIC), and Google remote procedure call (GRPC) have been appropriately adopted for the WDN. To demonstrate the system performance of the WDN, a blockchain system was fully implemented in the form of docker containers. Furthermore, we verified the feasibility of the WDN by developing decentralized applications (DApps).

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 collaborative virtual 3D object modeling system for mobile augmented reality (AR) streaming services over 5G networks. The objective of the proposed system is to provide AR streaming services with low latency and high virtual object quality by effectively leveraging mobile edge cloud (MEC) and device-to-device (D2D) communication. MEC is utilized to perform computation-intensive 3D object modeling, such as virtual object slicing and mesh simplification, with abundant computing and storage resources. D2D communication is also utilized to reduce transmission delay by sharing virtual objects of interest among adjacent users. To achieve this goal, we propose a part-segment quality selection algorithm that can control the quality of each part segment and the transmission source by considering the user's network condition. In the proposed system, all elements in the AR cloud, MEC, and mobile device are technically integrated to work together seamlessly. Finally, the proposed system is fully implemented using well-known open-source frameworks, including WebGL and AR.js. The system was then examined in real wireless network environments. The experimental results demonstrate the performance of the proposed system. The proposed system outperforms conventional systems in terms of service latency and visual 3D object quality.

Web 3.0 is an emerging Internet paradigm based predominantly on the blockchain technology. Because Web 3.0 applications are designed to operate over trustless and permissionless networks, they can have significant advantages, such as decentralized control structures and transparency. Hence, existing web applications are being reproduced using Web 3.0 technologies. In contrast, real-time services are still implemented with the Web 2.0 architecture. In particular, implementing Web 3.0 media streaming requires modifications to the service architecture of existing media streaming systems because some technical difficulties exist. For example, as data moves from centralized data centers to distributed storage, the user's quality of experience may be severely degraded. In addition, the software components comprising the Web 3.0 stack, such as interplanetary file system, cryptocurrency wallets, and the Ethereum JavaScript API are not compatible with various combinations of OSs, media players, and browsers. Therefore, in this study, we propose an end-to-end system architecture designed for Web 3.0 real-time services, which prevents degradation of service quality. Further, we present a media NFT marketplace named Retriever (https://retriever.live) fully developed using Web 3.0 technologies. Retriever allows users to enjoy watching video content and further to directly trade their content without intermediaries by ensuring the privacy of the data and managing digital intellectual property. In particular, Retriever does not sacrifice the user experience and is compatible with multiple mobile devices.

We propose a service-aware dynamic sharding approach for enhancing the scalability of blockchain platforms. The objective of our proposed approach is to decrease the service latency for user transactions and increase the transaction throughput of conventional blockchain platforms. Sharding is a well-known solution to the problem of blockchain scalability. In sharded blockchains, transactions can be processed in parallel on different shards. However, compared to intra-transactions, cross-shard transactions, which are inevitably generated in sharded blockchains, are confirmed after a relatively long time. This attribute may result in the overall degradation of performance. To mitigate the effects of cross-shard transactions on the performance of blockchain systems, our proposed approach dynamically relocates the data accessed through cross-shard transactions to the same shard. Further, two or more highly related shards are adaptively merged into a single shard, or a single shard is split into smaller disjointed shards to fully utilize shards in parallel. To verify the efficacy of our proposed approach, we implemented a sharded blockchain using the Hyperledger-Fabric platform based on the design concepts of existing sharded blockchains. The experimental results show that the proposed approach can improve the scalability of existing blockchains while maintaining the consistency of ledgers during dynamic state changes.

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 an effective adaptive 360° video VR streaming system based on the Spatial Representation Description (SRD) feature of MPEG-DASH. The goal of the proposed system is to provide a seamless 360° video VR streaming service with high spatial quality over the time-varying wireless network. To achieve this goal, the proposed system adopts tile-based viewport-adaptive streaming that is a promising way to efficiently deliver 360° video. In accordance with the SRD information and user’s head movement, the proposed system is designed to enhance the quality of the user experience by intensively allocating a limited bit budget to specific segments of interest without noticeable blanks. Additionally, we propose a fast recovery algorithm to minimize the effect of blanks caused by unpredictable head movement. Moreover, the proposed system considers the binocular disparity of the human eye to improve the subjective quality of stereo video. The proposed system is fully implemented by using well-known open sources (e.g., OpenHMD, Kvazaar and GPAC), C/C++, Python and head-mounted display (HMD) equipment. Finally, the experimental results are provided to verify the performance of the proposed system in real wireless 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 paper presents a cooperative base station caching and X2 link traffic offloading system for video streaming services over software-defined networking (SDN)-enabled 5G networks. The goal of the proposed system is to provide seamless video streaming services to users while alleviating the backhaul traffic in the cellular network by effectively caching contents at the base stations and appropriately offloading parts of the backhaul traffic to X2 links. In the proposed system, the main functionalities of content-centric networking (CCN) are deployed at the base stations to take advantage of the attributes offered by CCN such as in-network caching and name-based forwarding. Furthermore, SDN technology is adopted not only to solve the practical issues of CCN, but also to control network resources efficiently. The proposed system is designed to determine near optimal caching positions and routes for all content in two phases to reduce the computational complexity for the real-time processing. The proposed system is fully implemented using well-known open sources, e.g., CCNx and ONOS, and C/C++. Finally, the system is examined in real wireless network environments. During the experiment, the proposed system can reduce cellular traffic by more than 13 percent over existing traffic offloading algorithms.

This work presents a mobile data offloading system for video streaming services over software-defined networking (SDN)-enabled wireless networks. The goal of the proposed system is to alleviate cellular network congestion by offloading parts of video traffic to a WiFi network while improving video quality of all users by efficiently and fairly sharing the limited long term evolution (LTE) resources. In the proposed system, SDN architecture is applied to the wireless network environment to quickly react to time-varying network conditions and finely control the amount of traffic transmitted through LTE and WiFi networks. Under the SDN-enabled wireless environment, we frame the mobile data offloading problem for video streaming services as an asymmetric Nash bargaining game to address conflict among competitive mobile users. Furthermore, we propose a resource allocation algorithm that pursues an effective trade-off between global system utility and quality-of-service fairness among users. The system is fully implemented using ONOS SDN controller and Raspberry PI-3-based mobile devices, and performance is evaluated over real wireless networks.

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.

This paper presents a game-theoretic scalable offloading system that provides seamless video streaming services by effectively offloading parts of video traffic in all video streaming services to a WiFi network to alleviate cellular network congestion. The system also consolidates multiple physical paths in a cost-effective manner. In the proposed system, the fountain encoding symbols of compressed video data are transmitted through long term evolution (LTE) and WiFi networks concurrently to flexibly control the amount of video traffic through the WiFi network as well as mitigate video quality degradation caused by wireless channel errors. Furthermore, the progressive second price auction mechanism is employed to allocate the limited LTE resources to multiple user equipment in order to maximize social welfare while converging to the ε-Nash equilibrium. Specifically, we design an application-centric resource valuation that explicitly considers both the realistic wireless network conditions and characteristics of video streaming services. In addition, the scalability and convergence properties of the proposed system are verified both theoretically and experimentally. The proposed system is implemented using network simulator 3. Simulation results are provided to demonstrate the performance improvement of the proposed system.

This paper presents a smart base station-assisted partial-flow device-to-device offloading system that provides seamless video streaming services to clients by effectively offloading parts of the video traffic to D2D networks in order to alleviate the cellular network traffic load. In the proposed system, the main functionalities of the content centric networking (CCN) technology are deployed for efficient and fast cached content management at the base stations and in the devices. First, a logical overlay Chord-like network is constructed among the smart base stations for effectively handling significant numbers of the distributed contents. Then, content management protocols over the Chord-like network are implemented for rapidly finding the D2D server that contains the cached content requested by the D2D client. A two-stage PID-based LTE traffic controller is proposed to determine the amount of traffic to be offloaded to the D2D network among the cellular operator, the D2D servers, and the D2D clients. The proposed system is fully implemented using a CCNx open source and C/C++. Experimental results are provided to demonstrate the performance improvement of the proposed system.

This work presents a control-theoretic scalable device-to-device offloading system that provides seamless video streaming services to clients by effectively offloading parts of the video traffic to D2D networks in order to alleviate the cellular network traffic load. In the proposed system, the main functionalities of the content centric networking (CCN) technology are employed to simultaneously download the video content from multiple wireless networks. A two-stage PID-based LTE traffic controller is proposed to determine the amount of traffic to be offloaded to the D2D network among the cellular operator, the D2D servers, and the D2D clients. The proposed system is fully implemented using a CCNx open source and C/C++. Experimental results are provided to demonstrate the performance improvement of the proposed system.