Virtual Private Network (VPN) is a cost effective method to provide integrated multimedia services. Usually heterogeneous multimedia data can be categorized into different types according to the required Quality of Service (QoS). Therefore, VPN should support the prioritization among different services. In order to support multiple types of services with different QoS requirements, efficient bandwidth management algorithms are important issues. In this paper, I employ the Kalai-Smorodinsky Bargaining Solution (KSBS) for the development of an adaptive bandwidth adjustment algorithm. In addition, to effectively manage the bandwidth in VPNs, the proposed control paradigm is realized in a dynamic online approach, which is practical for real network operations. The simulations show that the proposed scheme can significantly improve the system performances.

In this paper, a new adaptive online price control scheme is formalized based on the Stackelberg game model. To provide the most desirable network performance, the proposed scheme consists of two different control mechanisms; user-based and operator-based mechanisms. By using the hierarchical interaction strategy, control decisions in each mechanism act cooperatively and collaborate with each other to satisfy conflicting performance criteria. With a simulation study, the proposed scheme can adaptively adjust the network price to approximate an optimized solution under widely diverse network situations.

In this paper, a new bandwidth allocation scheme is proposed based on the Mechanism Design (MD); MD is a branch of game theory that stimulates rational users to behave cooperatively for a global goal. The proposed scheme consists of bandwidth adaptation, call admission control and pricing computation algorithms to improve network performance. These algorithms are designed based on the adaptive online approach and work together to maximize bandwidth efficiency economically. A simulation shows that the proposed scheme can satisfy contradictory requirements and so provide well-balanced network performance.

In this paper, a new power control scheme is proposed to maximize the network throughput with fairness provisioning. Based on the Stackelberg game model, the proposed scheme consists of two control mechanisms; user-level and system-level mechanisms. Control decisions in each mechanism act cooperatively and collaborate with each other to satisfy efficiency and fairness requirements. Simulation results demonstrate that the proposed scheme has excellent network performance, while other schemes cannot offer such an attractive performance balance.

New multimedia services over cellular/WLAN overlay networks require different Quality of Service (QoS) levels. Therefore, an efficient network management system is necessary in order to realize QoS sensitive multimedia services while enhancing network performance. In this paper, we propose a new online network management framework for overlay networks. Our online approach to network management exhibits dynamic adaptability, flexibility, and responsiveness to the traffic conditions in multimedia networks. Simulation results indicate that our proposed framework can strike the appropriate balance between performance criteria under widely varying diverse traffic loads.

Recently, cooperative spectrum sensing is being studied to greatly improve the sensing performance of cognitive radio networks. To develop an adaptable cooperative sensing algorithm, an important issue is how to properly induce selfish users to participate in spectrum sensing work. In this paper, a new cognitive radio spectrum sharing scheme is developed by employing the trust-based bargaining model. The proposed scheme dynamically adjusts bargaining powers and adaptively shares the available spectrum in real-time online manner. Under widely different and diversified network situations, this approach is so dynamic and flexible that it can adaptively respond to current network conditions. Simulation results demonstrate that the proposed scheme can obtain better network performance and bandwidth efficiency than existing schemes.

Bandwidth is an extremely valuable and scarce resource in multimedia networks. Therefore, efficient bandwidth management is necessary in order to provide high Quality of Service (QoS) to users. In this paper, a new QoS-aware bandwidth allocation algorithm is proposed for the efficient use of available bandwidth. By using the multi-objective optimization technique and Talmud allocation rule, the bandwidth is adaptively controlled to maximize network efficiency while ensuring QoS provisioning. In addition, we adopt the online feedback strategy to dynamically respond to current network conditions. With a simulation study, we demonstrate that the proposed algorithm can adaptively approximate an optimized solution under widely diverse traffic load intensities.

In this letter, we propose a new energy efficient online routing algorithm for QoS-sensitive sensor networks. An important design principle underlying our algorithm is online decision making based on real time network estimation. This on-line approach gives adaptability and flexibility to solve a wide range of control tasks for efficient network performance. In addition, our distributed control paradigm is practical for real sensor network management. Simulation results indicate the superior performance of our algorithm between energy efficiency and QoS provisioning.

The past decade has seen a surge of research activities in the fields of mobile computing and wireless communication. In particular, recent technological advances have made portable devices, such as PDA, laptops, and wireless modems to be very compact and affordable. To effectively operate portable devices, energy efficiency and Quality of Service (QoS) provisioning are two primary concerns. Dynamic Voltage Scaling (DVS) is a common method for energy conservation for portable devices. However, due to the amount of data that needs to be dynamically handled in varying time periods, it is difficult to apply conventional DVS techniques to QoS sensitive multimedia applications. In this paper, a new adaptive DVS algorithm is proposed for QoS assurance and energy efficiency. Based on the repeated learning model, the proposed algorithm dynamically schedules multimedia service requests to strike the appropriate performance balance between contradictory requirements. Experimental results clearly indicate the performance of the proposed algorithm over that of existing schemes.