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 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 an effective mobile cloud-based interactive 3D rendering and streaming system with data-service cost constraints over heterogeneous wireless networks. The proposed system contains software-defined networking (SDN)-enabled adaptive cloud resource management module and context-aware 3D rendering and streaming module to enhance the quality-of-service of interactive 3D rendering and streaming services. The first module is designed to efficiently control the limited cloud resources, such as server computing power and available backbone link bandwidth, and the second module is implemented to pursue an effective tradeoff between 3D rendering quality and compressed image quality. The system is implemented using Mininet network emulator with an OpenDay-light SDN controller, and VirtualGL with an online open-source 3D game, PlaneShift. Finally, the system is examined at a mobile device connected to real long-term evolution and WiFi networks.

In this work, we present an avatar mobility and network condition-aware 3D game over wireless networks. The proposed system is designed to pursue an effective tradeoff between 3D rendering quality and compressed image quality. By considering user-preferred networking cost (i.e., data transmission charges), 3D rendering level and image compression parameters are determined dynamically in order to enhance the quality of service for interactive 3D game. The system is implemented using VirtualGL and an online open-source 3D game, PlaneShift, and examined using a mobile device connected to LTE and WiFi networks.

In this work, we present a SDN-based adaptive cloud network management for 3D rendering and streaming services. In the proposed system, SDN is adopted to provide flexible and intelligent control of cloud network resources. By considering server load and network link states simultaneously, 3D graphic rendering servers, encoding servers, and data transmission paths are dynamically determined with low computational complexity. The proposed system is implemented using the OpenDaylight SDN controller and Mininet network emulator. Finally, simulation results are provided to demonstrate the performance of the proposed system.

In this work, we propose a social relation-aware SVC-based video streaming system over Peer-to-Peer network. In the proposed system, peers efficiently control the limited resources because video streaming services require significantly more network resources than other traditional data services. The proposed system is designed to take into account social relations as well as video quality. Social relation information is obtained from Social Network Services, and peer resources are assigned according to social relation-aware friendship and credit models. In addition, SVC is adopted to support various network environments. Finally, experimental results are provided to evaluate the performance of the proposed system.

In this work, a networking cost effective video streaming system over heterogeneous wireless networks is proposed. The goal of the proposed system is to provide a high-quality video streaming service with minimum networking cost (i.e., data transmission charges). To overcome performance limitations of a single network, multiple physical paths are concurrently exploited, and the number of packets transmitted through selected paths is determined by considering the user preferred video quality and the networking cost for packet transmission. The proposed system is fully implemented on Java and C/C++, and tested over real WiFi and LTE networks. Experimental results show that the proposed system provides a high-quality video streaming service at relatively lower networking cost compared to existing systems.