Game-theoretic Scalable Mobile Data Offloading System for Video Streaming Services over Heterogeneous Wireless Networks

Abstract

Driven by the rapid increase in smartphones, tablets, laptops, and the wide usage of bandwidth-intensive multimedia applications such as YouTube and Netflix, the growth in global mobile data traffic has been tremendous. Mobile data offloading has emerged as a major solution to accommodate the massive growth in mobile data traffic. The foremost challenge associated with mobile data offloading is service continuity and consistent user experience. However, it is not easy to maintain the quality-of-experience of a video streaming service when mobile data offloading is performed due to performance limitations in data rate and service range needed to deliver high-quality video streaming services in a single wireless network environment. In addition, in a large-scale decentralized and time-varying wireless environment, it is difficult to share cellular network resource among competitive mobile users. In this dissertation, we propose a game-theoretic scalable mobile data offloading system for video streaming services over heterogeneous wireless networks to alleviate cellular network congestion while improving video quality of all users by fairly and efficiently assigning limited cellular network resources. First, we present a progressive second price (PSP) auction-based mobile data offloading system which 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. In the proposed system, the PSP auction mechanism is employed to allocate the limited long term evolution (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. The proposed system is implemented using network simulator 3. Large-scale deployment of the offloading system is one of the major challenges because the current cellular network architecture is difficult to expand or manage, inflexible, and too costly. Thus, we apply a software-defined networking (SDN) architecture to 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 as an asymmetric Nash bargaining game. Compared to the resource allocation through iterative auction, which may cause an additional delay until a convergence, the proposed bargaining-theoretic resource allocation by the SDN controller can quickly derive offloading policies that optimize network utilization and improve video quality of all users. Furthermore, we propose a bargaining power control algorithm which 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.