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.

An adaptive service framework is expected to support real-time multimedia services in wirless/mobile cellular networks with various classes of traffic and diverse bandwidth requirements. Quality of service (QoS) provisioning in an adaptive framework is another challenging consideration, such as quantifying the level of bandwidth degradation of an ongoing calls and guaranteeing stable QoS levels. Considering both the period and the depth of degradation, the degradation area ratio (DAR) represents the average ratio of a call's degradation and is one of the meaningful measures for adaptive service in call level analysis. In this paper, analytical models for estimating the DAR and finding the optimal control parameters are presented in multi-class traffic call management situations. In complete partitioning capacity based threshold-type call admission control (CAC), a one-dimensional Markov chain with an absorbing state is proposed for estimating the DAR in each traffic class. We formulate a two-leveled optimization problem minimizing the total blocking probabilities subject to QoS requirements and present the procedures required in finding the optimal capacities and threshold values by using modified dynamic programming. In complete sharing capacity based threshold-type CAC, the multidimensional Markov model is approximately reduced to a one-dimensional model in order to reduce complexity and hence calculation time. The reduced model is compared with multidimensional Markov model in numerical examples. The optimization problem is formulated minimizing the total blocking probabilities subject to QoS requirements and the optimal threshold parameters are found by using a genetic algorithm. Performance of two adopted admission policies in adaptive framework situations is illustrated by numerical results.

A new transmit permission control scheme applicable in multi-cell communication systems is proposed. In this scheme, by prohibiting the transmissions from the users with relatively high propagation loss to their connecting hub stations, level of multiple access interference is decreased, and hence throughput characteristics are improved. Moreover, we continue our discussion to propose two adaptive forms of the transmit permission control scheme, in which the prohibition condition becomes more intelligent by considering the level of the offered traffic loads to hub stations. These methods are utilized in a slotted Aloha random transmission of the spread spectrum packets, and on the uplinks of a low earth orbit satellite communication system as an example of the multi-cell systems. It is shown that the adaptive schemes exhibits significantly improved characteristics at all offered traffic loads in these systems.