text: This paper studies the problem of utility maximization for clients with delay based QoS requirements in wireless networks. We adopt a model used in a previous work that characterizes the QoS requirements of clients by their delay constraints, channel reliabilities, and delivery ratio requirements. In this work, we assume that the utility of a client is a function of the delivery ratio it obtains. We treat the delivery ratio for a client as a tunable parameter by the access point (AP), instead of a given value as in the previous work. We then study how the AP should assign delivery ratios to clients so that the total utility of all clients is maximized. We apply the techniques introduced in two previous papers to decompose the utility maximization problem into two simpler problems, a CLIENT problem and an ACCESS-POINT problem. We show that this decomposition actually describes a bidding game, where clients bid for the service time from the AP. We prove that although all clients behave selfishly in this game, the resulting equilibrium point of the game maximizes the total utility. In addition, we also establish an efficient scheduling policy for the AP to reach the optimal point of the ACCESS-POINT problem. We prove that the policy not only approaches the optimal point but also achieves some forms of fairness among clients. Finally, simulation results show that our proposed policy does achieve higher utility than all other compared policies.
  @cite_4: 
  @cite_7: In this paper, we study utility maximization problems for communication networks where each user (or class) can have multiple alternative paths through the network. This type of multi-path utility maximization problems appear naturally in several resource allocation problems in communication networks, such as the multi-path flow control problem, the optimal quality-of-service (QoS) routing problem, and the optimal network pricing problem. We develop a distributed solution to this problem that is amenable to online implementation. We analyze the convergence of our algorithm in both continuous-time and discrete-time, and with and without measurement noise. These analyses provide us with guidelines on how to choose the parameters of the algorithm to ensure efficient network control.
  @cite_9: Adaptive quality-of-service (QoS) techniques can effectively respond to time-varying channel conditions found in wireless networks. In this paper, we assess the state-of-the-art in QoS adaptive wireless systems and argue for new adaptation techniques that are better suited to respond to application-specific adaptation needs. A QoS adaptive data link control model is presented that accounts for application-specific adaptation dynamics that include adaptation time scales and adaptation policies. A centralized adaptation controller employs a novel utility-fair bandwidth allocation scheme that supports the dynamic bandwidth needs of adaptive flows over a range of operating conditions. Three wireless service classes play an integral role in accommodating a wide variety of adaptation strategies. In this paper, we discuss the design of the utility-fair allocation scheme and the interaction between the centralized adaptation controller and a set of distributed adaptation handlers, which play a key role in intelligently responding to the time-varying channel capacity experienced over the air-interface.
  @cite_3: 
  @cite_2: This paper addresses the issues of charging, rate control and routing for a communication network carrying elastic traffic, such as an ATM network offering an available bit rate service. A model is described from which max-min fairness of rates emerges as a limiting special case; more generally, the charges users are prepared to pay influence their allocated rates. In the preferred version of the model, a user chooses the charge per unit time that the user will pay; thereafter the user's rate is determined by the network according to a proportional fairness criterion applied to the rate per unit charge. A system optimum is achieved when users' choices of charges and the network's choice of allocated rates are in equilibrium.
  @cite_10: In this paper we propose a general utility-oriented adaptive quality of service (QoS) model for wireless networks and establish a framework for formulating the bandwidth allocation problem for users with time-varying links. For slow link variations, it is inadequate to only employ low-level adaptive mechanisms at the symbol or packet level, such as error correction coding or swapping packet transmission opportunities. To improve bandwidth utilization and satisfy users' QoS requirements, high-level adaptive mechanisms working at larger time scale are needed. We propose an adaptive bandwidth allocation scheme, which is capable of providing QoS guarantees, ensuring long-term fairness, and achieving high bandwidth utilization. A finite-state Markov channel model (FSMC) is used to model wireless links.
summary: There is also research on utility maximization for both wireline and wireless networks. Kelly @cite_2 and Kelly, Maulloo, and Tan @cite_4 have considered the rate control algorithm to achieve maximum utility in a wireline network. Lin and Shroff @cite_7 has studied the same problem with multi-path routing. As for wireless networks, Xiao, Shroff, and Chong @cite_3 has proposed a power-control framework to maximize utility, which is defined as a function of the signal-to-interference ratio and cannot reflect channel unreliability. Cao and Li @cite_10 has proposed a bandwidth allocation policy that also considers channel degradation. Bianchi, Campbell, and Liao @cite_9 has studied utility-fair services in wireless networks. However, all the aforementioned works assume that the utility is only determined by the allocated bandwidth. Thus, they do not consider applications that require delay bounds.