Cost- and Energy-Aware Multi-Flow Mobile Data Offloading Using Markov Decision Process

With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues, MUs should be able to decide whether to offload their traffic to a complementary wireless LAN. Our previous work studied single-flow wireless LAN offloading from a MU's perspective by considering delay-tolerance of traffic, monetary cost and energy consumption. In this paper, we study the multi-flow mobile data offloading problem from a MU's perspective in which a MU has multiple applications to download data simultaneously from remote servers, and different applications' data have different deadlines. We formulate the wireless LAN offloading problem as a finite-horizon discrete-time Markov decision process (MDP) and establish an optimal policy by a dynamic programming based algorithm. Since the time complexity of the dynamic programming based offloading algorithm is still high, we propose a low time complexity heuristic offloading algorithm with performance sacrifice. Extensive simulations are conducted to validate our proposed offloading algorithms

Game-theoretic Scalable Offloading for Video Streaming Services over LTE and WiFi 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 epsilon-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.111Nsciescopu

Enabling Energy-Aware Collaborative Mobile Data Offloading for Smartphones

Abstract—Searching for mobile data offloading solutions has been topical in recent years. In this paper, we present a collaborative WiFi-based mobile data offloading architecture-Metropolitan Advanced Delivery Network (MADNet), targeting at improving the energy efficiency for smartphones. According to our measurements, WiFi-based mobile data offloading for moving smartphones is challenging due to the limitation of WiFi antennas deployed on existing smartphones and the short contact duration with WiFi APs. Moreover, our study shows that the number of open-accessible WiFi APs is very limited for smartphones in metropolitan areas, which significantly affects the offloading opportunities for previous schemes that use only open APs. To address these problems, MADNet intelligently aggregates the collaborative power of cellular operators, WiFi service providers and end-users. We design an energy-aware algorithm for energyconstrained devices to assist the offloading decision. Our design enables smartphones to select the most energy efficient WiFi AP for offloading. The experimental evaluation of our prototype on smartphone (Nokia N900) demonstrates that we are able to achieve more than 80 % energy saving. Our measurement results also show that MADNet can tolerate minor errors in localization, mobility prediction, and offloading capacity estimation. I