Adaptive Resource Allocation for Multimedia QoS ... - Semantic Scholar

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 2, MARCH 2004

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Adaptive Resource Allocation for Multimedia QoS Management in Wireless Networks Lei Huang, Member, IEEE, Sunil Kumar, Member, IEEE, and C.-C. Jay Kuo, Fellow, IEEE

Abstract—Adaptive resource allocation for multimedia quality of service (QoS) support in broadband wireless networks is examined in this work. A service model consisting of three service classes with different handoff-dropping requirements is presented. Appropriate call-admission control and resource-reservation schemes are developed to allocate resources adaptively to the real-time service classes with a stringent delay bound. Moreover, we propose an effective and efficient measurement-based dynamic resource allocation scheme to meet the target handoff-dropping probability. The nonreal-time applications, serviced by the best-effort model, are supported. The system accommodates adaptive multimedia applications to further reduce the blocking and dropping probabilities of real-time applications. Based on a multidimensional model analysis, simulations are conducted to evaluate the system performance. The simulation results show that the proposed system can satisfy the desired QoS of multimedia applications under different traffic loads, while achieving high utilization. Index Terms—Admission control, cellular network, handoff, quality of service (QoS), resource reservation, service model, wireless multimedia network.

I. INTRODUCTION

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UTURE broad-band wireless networks, such as the general packet radio system (GPRS) and the universal mobile telecommunications system (UMTS), will extend current second generation (2G) voice-based wireless services to broad-band multimedia services through packet-switched technology. Compared with wired networks, wireless networks provide more freedom to communications at the cost of a lower bandwidth, higher latency, and a higher burst error rate. Providing multimedia services with a quality of service (QoS) guarantee in such an environment presents more challenges due to the limited bandwidth resource, the highly variable environment, and user’s mobility. To address this complex problem, QoS in wireless networks is considered at two levels, i.e. the application level and the connection level. Application-level QoS is related to perceived quality at the user end and is commonly considered Manuscript received March 2, 2002; revised July 10, 2003 and October 17, 2003. L. Huang is with Department of Electrical Engineering and Computer Science, Loyola Marymount University, Los Angeles, CA 90045 USA (e-mail: [email protected]). S. Kumar is with Department of Electrical and Computer Engineering—Systems, Clarkson University, Potsdam, NY 13699 USA (e-mail: [email protected]). C.-C. J. Kuo is with Integrated Media Systems Center and the Department of Electrical Engineering Systems, University of Southern California, Los Angeles, CA 90089-2564 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TVT.2003.823290

in packet-switched networks. A set of parameters, such as delay/delay jitter, error/loss and throughput, etc., are used to describe application-level QoS. Since packet-switched networks take advantage of a higher degree of multiplexing among services, packets for a certain service flow may experience varying delay, delay jitter, and loss. Efficient packet-access protocols and packet-scheduling schemes play key roles in solving these QoS problems. Connection-level QoS is related to connection establishment and management. It measures the connectivity and continuity of service in a wireless network, mostly by two parameters: the new-call-blocking probability, which measures service connectivity, and the handoff-dropping probability, which measures service continuity during handoff. For a mobile user, dropping an ongoing call is generally more unacceptable than blocking a new call request. Therefore, minimizing the handoff-dropping probability is usually a main objective in the wireless system design. On the other hand, the goal of a network service provider is to maximize the revenue by improving network resource utilization, which is usually associated with minimizing the new-call-blocking probability while keeping the handoff dropping below a certain threshold. In recent years, there has been increasing research interest in supporting connection-level, as well as application-level, QoS for multimedia applications in wireless networks. Different call-admission control and resource-reservation schemes have been proposed to reduce the handoff-dropping probability and/or the new-call-blocking probability. One of the first bandwidth-eservation schemes for handoff was introduced in mid 1980s [1]. In this scheme, a set of channels are permanently reserved—exclusively for handoff calls—to keep the handoff-dropping probability lower than the new-call-blocking probability. It was shown that this reservation scheme was optimal to minimize a linear objective function of these two probabilities under certain assumptions [2]. However, static reservation is not efficient for varying traffic conditions found in wireless networks. Lately, several distributed call-admission-control schemes have been proposed to dynamically calculate the required bandwidth in order to maintain a low cell-overload probability [3], [4]. However, the statistical models used in calculation were not realistic. Moreover, these schemes were designed based on traditional mobile networks with only voice traffic. Thus, they cannot effectively handle a variety of connection bandwidths, traffic loads, and user’s mobility. In [5], the concept of shadow cluster was introduced for resource reservation and admission control to reduce the call-dropping probability by predictive resource allocation. In this scheme, a shadow cluster represents a set of cells around an active mobile. However, how to determine

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the shadow cluster was not explained clearly. Moreover, this scheme required each base station in the shadow cluster to predict future resource demands according to the information about active mobile users’ bandwidth requirement, position, movement pattern, and time. Consequently, it is computationally too expensive to be practical. Oliveira et al. [6] proposed an admission-control scheme for wireless multimedia applications by considering two types of traffic, i.e. real-time and nonreal-time. This scheme dynamically reserves bandwidth in cells surrounding the one in which the connection originated, to provide QoS guarantees in high-speed multimedia wireless networks. However, bandwidth reservation in all neighboring cells is a waste of resource, as the mobile user hands off to only one of them. Another admission-control scheme was proposed based on a three-class service model for integrated service packet networks with mobile hosts [7]. In this scheme, each mobile host that is requesting a new connection has to provide its accurate mobility specification, which consists of the set of cells the mobile host is expected to visit during its lifetime. This limits the flexibility gained from mobility. In contrast, most of the research effort on application-level QoS focuses primarily on the wireless local area network (WLAN) environment, where the connection-level QoS problem is often ignored [8]–[10]. In this paper, we propose a system to provide appropriate QoS according to service requests from end users, under the constraint of limited and varying bandwidth resources. The main features of the proposed system are highlighted as follows. • It is based on a comprehensive service model consisting of three service classes (i.e. handoff-guaranteed, handoffprioritized, and best-effort). • It deploys different resource-reservation schemes adaptively for real-time service classes (i.e., handoffguaranteed and handoff-prioritized) to guarantee their connection-level QoS through a connection-oriented virtual-circuit service. • It uses an efficient dynamic call-admission-control scheme to meet the target handoff-dropping probability of real-time services. • It exploits the rate-adaptive feature of multimedia applications to further improve the efficiency of resource utilization. The rest of this paper is organized as follows. The proposed system for QoS provisioning is described in Section II. A measurement-based dynamic guard-channel scheme is developed to achieve the target handoff-dropping probability in Section III. Some mathematical analysis for the proposed system is presented in Section IV and simulation results are provided in Section V. Finally, concluding remarks are given in Section VI.

II. PROPOSED ADAPTIVE QOS MANAGEMENT SYSTEM A wireless communication network typically consists of a fixed network backbone and a wireless access system. The fixed network part, through mobile switching centers (MSC), provides connections between radio-access ports, often called base stations (BS). The BS in turn provides wireless connections

Fig. 1. Block diagram of our proposed QoS management system.

to mobile terminals (MT) located in its coverage areas (called cells). BS are distributed over the geographical area where communication services are covered. Continuous service coverage over a larger service area is achieved by handoff, which is the seamless transfer of a call from one BS to the other as the mobile unit crosses cell boundaries. The block diagram of our proposed QoS management system is illustrated in Fig. 1. With QoS as the kernel, the proposed system allows different applications to request different QoS from the network through a service model. Application profiles are mapped into the service model by different forms of traffic specifications. Network resources are adaptively allocated to different service classes by employing adaptive resource-allocation schemes, including call-admission control and resource reservation, according to the service model and QoS requirements. The adaptation module enables the negotiation of QoS between applications and networks whenever it is necessary. Each component is described in detail below. A. Service Model An appropriate service model that describes a set of offered services is the foundation of QoS provisioning [11]. Existing QoS-aware networks, such as ATM [12], InteServ [13], and DiffServ [14], designed their service models based on QoS requirements of applications in the corresponding network infrastructure. Based on this concept, we have designed our service model for multimedia applications in wireless networks as described below. First, multimedia applications are classified into real-time and nonreal-time applications according to their delay requirements. In order to achieve desired QoS for a real-time application, it is usually necessary to maintain a minimum bandwidth during its lifetime. We adopt the virtual circuit concept to establish connection for a real-time application request. The setup of a connection requires call-admission control and resource reservation to prevent network congestion and dropping of ongoing calls. We select the handoff-dropping probability as the primary QoS requirement and assume that it

HUANG et al.: ADAPTIVE RESOURCE ALLOCATION FOR MULTIMEDIA QoS MANAGEMENT IN WIRELESS NETWORKS

has a more significant impact on the overall connection-level QoS measurement. For nonreal-time applications, we use the best-effort service adopted in traditional IP networks. Data from these applications can be stored at a network node, such as the BS or an MT. Whenever the network has spare resources that are unused by real-time applications, these applications will be serviced under an appropriate scheduling algorithm. To improve resource utilization of the entire network, we also use resources reserved for real-time applications but not yet being in use to carry nonreal-time data. No call-admission control or resource reservation is required here. Based on our earlier discussion, we categorize applications to the following three classes. • Handoff-guaranteed service represents real-time applications that require absolute continuity, i.e., no handoff dropping is permitted before the call is completed. • Handoff-prioritized service represents real-time applications that can tolerate a reasonably low handoff-dropping probability. • Best-effort service represents nonreal-time applications that do not need a minimum bandwidth to set up a connection. B. Application Profile The above service model covers many application-level QoS aspects, such as delay, priority, and bandwidth adaptation, as well as pricing and mobility aspects. The network uses different application profiles for different service classes. For real-time service classes, including both handoff-guaranteed and handoff-prioritized services, the minimum required bandwidth to meet the delay requirement is necessary. The application profile also includes the required handoff-dropping probability for real-time service classes. For the handoff-guaranteed service, the target handoff-dropping probability should be 0. For the handoff-prioritized service, the target handoff-dropis bounded by . ping probability Moreover, an application requesting the handoff-guaranteed service should also provide its mobility information so that the network could predict the cells that the mobile is going to visit during its lifetime. For the best-effort service class, there is no minimum bandwidth requirement. In this class, the traffic load is described by the packet-generation rate and the packet size. The service model covers a wide range of applications. Some calls, such as emergency rescue or business transactions, cannot be dropped before completion. These applications will require the handoff-guaranteed service. Since priority is usually associated with pricing, some applications such as normal conversation, which are not so critical, may be willing to be served as handoff-prioritized service at a lower price. Another consideration could be user’s mobility, e.g., moving range and/or speed. Mobile users with high mobility move quickly within a large area across many cells, e.g., a moving vehicle on the highway. Handoff occurs frequently in this case. As a result, the probability that the call is dropped before its completion would be high even if the handoff-dropping probability in each individual cell is relatively low. Thus, users with high mobility

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would prefer the handoff-guaranteed service class or a lower target handoff-dropping probability. C. Resource Allocation For real-time service classes, resource allocation includes call-admission control (CAC) and resource-reservation (RR) mechanisms. These two mechanisms are closely related to each other to achieve the desired QoS for a given application. A different resource-allocation scheme, as explained below, is used for each service class to provide appropriate QoS to the corresponding applications. • For the handoff-guaranteed service class, it is necessary to reserve resources in other cells the mobile host may visit, which is indicated by its “application profile.” The reserved resources can only be used by the corresponding handoff-guaranteed call or the best-effort data call until the reserving handoff-guaranteed call arrives. This guarantees resources to each handoff-guaranteed call upon handoff. CAC is simply based on whether resources are reserved successfully. • For the handoff-prioritized service class, aggregate resources are reserved for the handoff calls of this class to maintain a reasonably low target handoff-dropping rate. We design a measurement-based algorithm that dynamically adjusts the threshold in the guard-channel scheme, which will be presented in Section III. • The best-effort service class is serviced with the remaining resources, including those reserved but not being used by real-time service classes. The above two real-time service classes can preempt this service class, thus improving the overall utilization of the network without sacrificing the QoS guarantee for real-time traffic. In our scheme, there are four types of real-time traffic listed in the decreasing order of priorities: the handoff-guaranteed handoff call (HGH), the handoff-prioritized handoff call (HPH), the handoff-prioritized new call (HGN), and the handoff-guaranteed new call (HPN). The resource sharing among the real-time traffic is performed by our resource-allocation scheme, as follows. The reservation for HGH calls is similar to the complete partitioning (CP) policy [15]. It ensures enough resource for admitted HGH calls. For HPH calls, a postreservation [15] for a lower handoff-dropping probability requirement is achieved by the proposed dynamic guard-channel scheme. The HPN and HGN calls adopt the complete sharing (CS) [15] policy to use the remaining available resources. Within each of the above four real-time traffic types, our resource-allocation scheme does not differentiate media types. For example, narrow-band voice and wide-band video belonging to the same traffic class will be treated equally. In other words, we use the CS policy among different media types within each traffic class. As indicated in [15], this scheme may discriminate against wide-band traffic, because a posteriori higher priority may be given to narrow-band applications, especially when the overall traffic load is heavy. Since we use the bandwidth adaptation feature of modern multimedia applications, this shortcoming would be alleviated to a great extent. Note that our system can be extended to employ other

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schemes, such as prereservation, to wide-band traffic according to different design objectives. To prevent bad calls that are violating their application profile from degrading the QoS of other conforming calls, a policing mechanism should be enforced by the network. In our system, if a call of the handoff-guaranteed service class tries to enter a cell not covered by its mobility pattern, it violates its service specifications. The policing mechanism will release the resources reserved for this call in all cells included in its application profile and treat it as a handoff-prioritized call. If this call still needs the handoff-guaranteed service, it has to make a new request with a new application profile. D. Rate-Adaptive Applications The wireless network is a highly variable environment where available link bandwidth may vary with network load and channel condition. By using rate-adaptive features of many multimedia applications, our proposed resource-allocation scheme can be adaptive to network conditions. For example, voice applications can be encoded at a rate ranging from 2 to 128 KB/s by choosing appropriate encoding mechanisms or dynamically modifying the encoding parameters. Similarly, video applications can be made rate adaptive by using a layered coding method. For example, the MPEG-2 video/audio compression standard [16] defines different layers and profiles to achieve SNR and spatial scalability. The lowest layer (i.e., the base layer) consists of critical information for decoding the image sequence at its lowest visual quality. Additional layers provide increasing quality. Another promising approach for adaptation is the use of embedded coding schemes, such as the wavelet-based JPEG-2000 image-coding standard [17]. Instead of a few discrete coding rates provided by a layered coding scheme, continuous bit rates can be achieved by cutting a single coded bit stream at almost any bit. Similarly, MPEG-4 [18], which is the new generation multimedia communication-coding standard, has the fine-granular scalability (FGS) mode. There has been a large amount of research in the bandwidth adaptation of multimedia services for wireless networks [19]–[23]. In our system, when rate-adaptive applications make a connection request to the network, they specify the range of bandwidths required to be supported by the network as , where and denote the minimum and maximum bandwidth requirements, respectively. Adaptation first takes place while admitting a new call. If the network has enough resources available, the request is admitted ; otherwise, it is admitted at a lower bandwidth. at cannot be satisfied, If the network is overloaded and the call is blocked. Bandwidth adaptation also takes place at the time of handoff occurrence. A rate-adaptive connection could be handed off at a lower rate if admitted at the cell it is entering is heavily loaded. On the other hand, a call admitted at could be upgraded to a higher rate if the cell it is going to enter is underutilized. We use the rate adaptation for a call only upon its admission and handoff, because frequent changes in the quality are not desirable for audio/visual applications.

Fig. 2.

State-transition diagram for the handoff-prioritized service.

III. MEASUREMENT-BASED DYNAMIC GUARD CHANNEL SCHEME In this section, we describe a measurement-based dynamic guard-channel scheme that is designed for the handoff-prioritized service class. Note that this scheme can be easily extended to any other system with the objective of achieving a target handoff-dropping probability. A. Guard-Channel Scheme Considering a single cell with a fixed amount of bandwidth capacity of channels, the traditional guard-channel scheme gives a higher priority to the handoff-call request as compared to the new call request by reserving a portion of the channel resource for handoff calls. More specifically, a new call request is channels occupied, admitted only when there are less than is a threshold between 0 and . On the other hand, where a handoff request is rejected only when all C channels are occhannels are the guard channels, cupied. As a result, which are used only by handoff calls. The new call and handoff requests to a given cell are Poisson and , respectively. The cell-residence processes with rate . time for each call is exponentially distributed with mean Each call requires one unit channel bandwidth. The arrivals of new- and handoff-call requests are independent of each other. Based on the above assumptions, a cell deploying the guardchannel scheme can be modeled by a M/M/C/C queuing system with a threshold state Th, as illustrated in Fig. 2. , The state space can be denoted by where is the number of occupied channels in the cell. The steady state distribution can be derived as [24] ,

(1)

where

(2) Given the steady state distribution, the new-call-blocking can be expressed as the probability of the probability or more channels are occupied, system in the states where i.e., (3)


The handoff-dropping probability channels are occupied, i.e.,

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is the probability that all

(4) Based on (3) and (4), it is obvious that

.

B. Proposed Dynamic Guard-Channel Scheme for the guardIt can be seen from the above analysis that channel scheme plays an important role in the new call-blocking and handoff-dropping probabilities. For a single results in less resources service-class network, increasing . At reserved for handoff calls, thus strictly increasing the decreases accordingly because more rethe same time, the sources become available for new arriving calls. On the other has the opposite effect [25]. Moreover, hand, decreasing the overall system utilization could also be influenced by the . Reserving more resources than needed by handoff value of calls results in lower system utilization since reserved resources is cannot be used by new call requests. Thus, the selection of should be selected so critical to system design. The value of that necessary and sufficient resources are reserved for handoff . Apparcalls to meet their QoS requirements in terms of ently, the selection of , i.e. the shared reservation of resources for handoff calls, should dynamically vary with changing traffic conditions. The above discussion is based on the assumption of a single traffic class. We extend it to multiple traffic classes by applying to reserve resources for HPH calls in a a single threshold postreservation fashion. The remaining resources are used by the new call requests from both HG and HP classes (i.e., HGN and HPN calls) in a CS fashion, as discussed in Section II-C. Dein our multiple traffic class network will reserve creasing the more resources for HPH calls. Unlike the single traffic network considered in [25], this will intuitively result in the lower or the for HPH calls and higher or the same for HGN and same has the opposite effect. HPN calls. Similarly, increasing Thus, the objective of our dynamic guard-channel scheme can be stated as follow. For a given system with a target for HPH calls, threshold handoff-dropping probability should be selected to keep the resulting for HPH calls as possible without exceeding it, while the as close to for new HGN and HPN calls should be minimized. In the following sections, we propose a scheme to achieve this goal. 1) Measurement of Handoff-Dropping Probability: Generally speaking, there are two approaches used to estimate the handoff-dropping probabilities: modeling based and measurement based. The modeling-based approach [26], [27] uses theoretical models to deduct the probability via mathematical analysis by assuming some parameters of the model. This approach gives a theoretical reference to the estimation such that the system can be designed in advance. However, the performance of this resulting algorithm depends on the conformance of the real system to the theoretical model and the accuracy of these assumptions made for those parameters. Generally, a real network system cannot be approximated by a simple model without making some

Fig. 3. Proposed dynamic guard-channel CAC algorithm.

unrealistic assumptions. Thus, more elaborate models are usually needed to make better estimation. However, the more elaborate the model is, the more complex it is to analyze and the more sensitive it is to the accuracy of assumptions. The measurement-based approach uses observed network conditions, obtained by some measurements, to do simple estimation. With this approach, system design is conducted during run time along with updated measurements. The performance of this approach is usually good considering its light computational complexity and its adaptability to changing conditions of practical networks. For these reasons, we design a measurement-based algorithm that aims at achieving the . objective defined above by dynamically adjusting Basically, our proposed dynamic call-admission scheme is based on measurements of the current . Compared with other measurement-based schemes relying on the measurement of current traffic conditions [3], [28], measuring the directly gives more accurate information and enables more efficient control in order to achieve our objective. The proposed scheme works as follows. At the beginning, an initial value of is selected for a given cell. The BS of this cell monitors its for HPH calls. When the measured reaches or exceeds the target value , is decreased by one unit (channel) so that more guard channels are reserved for handoff requests. Otherwise, we increase by one unit (channel) to admit more new call requests. The proposed algorithm is summarized with the pseudocodes given in Fig. 3, where is the currently occupied bandwidth and is the required bandwidth by the call request. Some design issues are discussed in the following subsections. 2) Update Frequency: An important design issue that affects the performance of the scheme is how often and when to update the measurement of to dynamically adjust . A frequent update can keep pace with changing traffic conditions. If the is not updated quickly enough to match the variation in system conditions, it could result in lower channel utilization

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(due to late increase in ) or higher (due to late decrease does not immediately affect in ). However, a new value of . Thus, very frequent updates could cause the measured unnecessary fluctuations and burden the system. Therefore, a tradeoff between fast response and system stability is desired. We use a “prompt-decreasing/timer-increasing” strategy to in the proposed choose the timing for updating the value of scheme. Whenever a handoff request is dropped, the BS checks and decreases if . the current value of higher Since only handoff-dropping events could drive the than the target value, this “prompt-decreasing” strategy can imto avoid mediately take the corrective action by decreasing further handoff drops. At the same time, a timer is set. If there are no further handoff drops upon the expiration of the timer, the . If it is true, the threshold is inBS checks if creased to improve channel utilization. Otherwise, it renews the timer. Before the timer’s expiration, if there are further handoff . If necessary, it further decreases drops, the BS again checks and resets the timer. 3) Timer Setting: The timer setting allows the effect of debeing reflected in the change of , since it may creasing take several successful handoff calls before the measured falls below the target or before it gets to the steady state. continues to drop (indicated The timer is renewed when the , which trigby no handoff dropping) until it falls below , or further handoff dropping happens gers an increase in , which means reserved resources are not when is needed. Thus, enough for handoff and a further decrease in and provides necesit avoids the unnecessary fluctuation of sary updating. The value of the timer is the maximum time that the system could be underutilized. 4) Initial Threshold: Another issue is the selection of the . Theoretically, it can be derived from mathinitial threshold ematical analysis conducted in the previous section according . This approach can quickly lead the system to given into a steady state. However, it requires modeling parameters such as the new call and the handoff-arrival rates, the cell-residence time, etc. Practically, when these parameters are to the capacity of the not available, we can simply set cell . By applying the proposed dynamic scheme, it will reach a steady state after some time. Starting with maximizes system utilization from the beginning. However, it bound might be achieved at the cost of violating the in the initial period. Note that the proposed CAC scheme, as illustrated in Fig. 3, is applied to the handoff-prioritized service class to achieve its target handoff-dropping probability in our system. However, the new call admission of the handoff-guaranteed service class is derived here. also subject to the dynamic changing threshold IV. ANALYSIS FOR REAL-TIME SERVICE CLASSES In this section, to further explain connection-level QoS provisioning for the real-time service class, we present mathematical analysis of our scheme. First, the handoff-guaranteed service class is modeled individually and then the system is modeled by considering both real-time service classes, i.e., handoff-guaranteed and handoff-prioritized services.

A. System With Handoff-Guaranteed Service Let us consider a single cell with a fixed amount of bandwidth (a total of channels of the same bandwidth); we derive the model for the handoff-guaranteed service. Let denote the number of cells a call is going to enter while traveling along a certain path during its lifetime. Upon admission, the channel resource is required to be reserved in each of the cells that it is going to enter. We define random variable as the channelcell, which occupation time of the call in the th is equal to the time from the admission of the call until it leaves the th cell. Thus, equals to the sum of independent random variables of the cell-residence time in cell , i.e., (5) Assuming that the cell-residence time is exponentially dis, it can be shown tributed with the mean cell-residence time has an -stage Erlangian distribution with the density that function (6) Here, the stage parameter decides the shape and moments of , this is the same as the the Erlangian distribution. For exponential distribution with the density function (7) The first cell is the originating cell where the resource is only occupied for the period of the cell-residence time, i.e. . It is assumed that the maximum number of cells a handoffguaranteed call can traverse is . Then, a cell with such a service class can be modeled as an -dimensional model, is for all the calls entering where dimension this cell as their th cell. Thus, the th dimension is an queuing model with the Poisson arrival and the -stage Erlang departure . Let the number . The state is the of calls in the th dimension be -tuple vector and the state space . is of the model, the Given the steady state distribution blocking probability in each dimension is (8)

The new-call-blocking probability of a handoff-guaranteed call, which would visit cells during its lifetime, can be computed as the probability that at least one of the cells has no available channel for the new call request. Under the assumption of a unified cell load in all cells, we have (9) Since handoff is guaranteed not to be dropped for this type of . calls, the handoff-dropping probability


Fig. 4. Comparison for adaptive and nonadaptive applications under a typical scenario. (a) Handoff-dropping probability of the HP class and (b) threshold.

B. System With Both Handoff-Guaranteed and Handoff-Prioritized Services channels offering both the Let us consider a cell with handoff-guaranteed and handoff-prioritized service classes described previously. The system can be analyzed by using a multidimensional model as follows. The state of a system is the , where and are the numbers of vector occupied (being used or reserved) channels by handoff-guaranteed and handoff-prioritized calls, respectively. Here, we channels exclusively for the handoff calls of reserve the handoff-prioritized service class. Therefore, the number of handoff-guaranteed service class calls is subject to the upper . The state space can be written as bound of

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Fig. 5. Comparison of the new-call-blocking probability for adaptive and nonadaptive applications under a typical scenario. (a) HG class and (b) HP class.

The state of the th subdimension denotes the number of . calls in this subdimension. Then, Assuming that the arrival and departure of each dimension are independent of each other, the steady state distribution can be obtained numerically. Given the steady state distribution and threshold for handoff-prioritized calls, we can derive the new-call-blocking probability and the handoff-dropping probability for both types of calls as follows. For the handoff-prioritized call, the new-call-blocking probfor a given cell is the probability that or more ability channels of this cell are occupied, i.e., (10)

and The handoff-guaranteed service can be divided into subdimensions, where the th subdimension has an -stage Erlangian departure , denoting that the handoffguaranteed call that is going to enter this cell as its th cell.

The handoff-dropping probability channels are being occupied, i.e.,

is the probability that all

(11)

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Fig. 6. Performance of real-time services for adaptive and nonadaptive applications under a typical scenario. (a) Handoff-dropping and (b) new-call-blocking probabilities.

Fig. 7. Channel utilization under a typical scenario. (a) Real-time service classes and (b) nonreal-time service class.

V. SIMULATION RESULTS For the handoff-guaranteed call, the new-call-blocking probis the probability that at least one of the cells ability it is going to enter has no available channel for the new call request. Under the assumption of a unified cell load in all cells, we have (12) is calculated in (10). Here, the handoff-dropping where probability . The above multidimensional M/Er/C/C queuing model is primarily used to explain the proposed system, especially in differentiating the handoff-dropping-probability requirements of different service classes. However, we are not aware of any generalized closed-form solution to such a system. Instead of providing an analytical solution, it is possible to obtain a numerical solution with queuing model software such as QTS [29]. An alternative numerical solution can also be obtained with discrete-event simulation, such as the OPNET simulator used in this paper, as described in the following section.

To evaluate the performance of the proposed QoS management system, a network model of a single cell with channel was constructed in OPNET, which is a discretecapacity event-driven simulator. Based on the analysis conducted in Section IV, a number of call generators generated Poisson arrivals of new and handoff call requests from different service classes. We use the following notations for the simulation parameters throughout this section. • For handoff-guaranteed service: : maximum number of cells a handoff-guaranteed — call traverses in its lifetime. : mean arrival rates of new call requests from — handoff-guaranteed calls entering this cell as its th cell. : mean of the exponentially distributed cell-resi— dence time of the handoff-guaranteed calls. Thus, the corresponding channel holding time of handoff-guaranteed calls have -stage Erlangian distributions, , and the mean channel holding times are .


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• For the handoff-prioritized service: : mean arrival rate of new call requests from — handoff-prioritized service class. : mean arrival rate of handoff requests from — handoff-prioritized service class. : mean of the exponentially distributed cell-resi— dence time of the handoff-prioritized calls. • For the best-effort service: : mean packet arrival (Poisson) rate. — : packet size in bytes per packet. — A. Typical Scenario To simulate a typical scenario, system parameters were set channels with as follows. The capacity of each cell is data rate 4800 b/s per channel. For the handoff-guaranteed ser, call/s, vice, call/s, and call/s, s. For the call/s, call/s, handoff-prioritized service, s. The target handoff-dropping probability of the handoff-prioritized service was set to 0.01. For the best-efpackets/s with fix-sized packets of fort service, B/packet. We simulated three types of real-time multimedia traffic, i.e., voice, audio, and video, each requiring one, two, and four channels from the network, respectively. Among the generated handoff-guaranteed and handoff-prioritized calls, we randomly select 50% as voice, 25% as audio, and the remaining 25% as video applications. The performance in terms of the new-call-blocking probability, the handoff-dropping probability, and channel utilization were compared for adaptive and nonadaptive applications. In the experiments for adaptive applications, audio and video calls were assumed to reduce their rate to one channel under congestion. The results for a 20-h simulation are shown in Figs. 4–7. We use HG and HP to denote the handoff-guaranteed and the handoff-prioritized service classes, respectively. By using our proposed dynamic guard-channel scheme for threshold selection, the HP service class achieves the target handoff-dropping probability after a short initial unstable stage, as shown in Fig. 4(a), for both adaptive and nonadaptive applications. The dynamic change of the threshold for achieving the target handoff-dropping probability is shown in Fig. 4(b). The value of the threshold fluctuates more for nonadaptive applications, since rate-adaptive applications have the ability to adapt to network conditions. There is no handoff dropping for HG service class due to bandwidth reservation. Figs. 5(a) and (b) show the new-callblocking probability for HG and HP service classes, respectively. The HG service has a higher new-call-blocking probability than does HP service, since it requires more resources to be reserved to ensure the zero-handoff-dropping probability. The comparison between adaptive and nonadaptive applications in Fig. 5 shows that the new call blocking of both real-time service classes are reduced significantly by exploiting the rate adaptability of multimedia applications. Similarly, the overall handoff-dropping probability [Fig. 6(a)] and new-call-blocking probability [Fig. 6(b)] of real-time services are lower for adaptive applications.

Fig. 8. Performance under varying real-time traffic load. (a) Handoff-dropping probabilities, (b) new-call-blocking probabilities, and (c) channel utilization.

Fig. 7 shows the channel utilization of real-time and nonreal-time service classes. The channel utilization of real-time service classes [Fig. 7(a)] increases slightly when incorporating adaptive applications. Due to resource reservation, real-time service classes cannot fully utilize channels to maintain the

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desired connection-level QoS. However, the nonreal-time best-effort service class can exploit the reserved but unused resources, as shown in Fig. 7(b), thus improving the overall utilization for both adaptive and nonadaptive cases. B. Varying Traffic Loads To investigate the system performance under different traffic loads, we conducted two sets of simulations, one with a varying real-time traffic load and the other with a varying nonreal-time traffic load, as shown in Figs. 8 and 9, respectively. In the first case, the new call arrival rate of the handoff-prioritized service was set to vary from 0.05 to 0.95 calls/s, while maintaining all other settings as those in the above typical scenario. The results are shown in Fig. 8. Fig. 8(a) shows that the handoff-dropping probabilities of HP and HG service classes remain at the target with increasing real-time traffic. Fig. 8(b) shows that the new-call-blocking probability of both real-time service classes increases with increasing real-time traffic. Fig. 8(c) shows that the bandwidth utilized by real-time service classes increases with the increase in the real-time traffic, while the throughput of nonreal-time traffic decreases. As a result, channel utilization in our scheme is quite high for real-time traffic, even without the best-effort service. This is achieved by the proposed dynamic guard-channel scheme and rate adaptation. Also, the decrease in the throughput of the best-effort class traffic shows that the real-time HG and HP traffic effectively preempts the ongoing best-effort traffic. of the best-effort In the second case, the packet arrival rate service was set to vary from 5 to 100 packets/s, while keeping all other parameters the same as the above typical scenario. The simulation results are shown in Fig. 9. We see from this figure that the traffic load of the nonreal-time service class does not degrade connection-level QoS parameters of real-time service classes, but influences the throughput of nonreal-time traffic itself. The results shown in Figs. 8 and 9 demonstrate that the system gives preference to real-time services to achieve their required QoS. On the other hand, the nonreal-time service improves the total utilization of network resources. Here, we assume that a mechanism exists in the multiple access control (MAC) layer to permit the best-effort class packet to occupy the unused channels, preempt them by real-time traffic, and take care of collisions. The results presented in Figs. 8(c) and 9(c) are obtained under the assumption of an ideal MAC layer and packet-scheduling policy, which does not sacrifice the channel utilization in order to resolve collision. Practically, the channel utilization by nonreal-time service and the resulting total channel utilization might be lower due to the limitation of practical MAC layer mechanism. C. Dynamic Guard-Channel Scheme Results in previous subsections show that the proposed dynamic guard-channel scheme can achieve the target handoff-dropping probability for real-time services under varying traffic loads. In this subsection, we further investigate the performance of the proposed scheme by setting different targets for the handoff-dropping probability of the HP service class, while the handoff-dropping probability for the HG class

Fig. 9. Performance under varying nonreal-time traffic load. (a) Handoffdropping probabilities, (b) new-call-blocking probabilities, and (c) channel utilization.

remains zero. The results are shown in Fig. 10. The nearly straight line in Fig. 10(a) indicates that our measurement-based dynamic guard-channel scheme can always achieve the target handoff-dropping probability of the handoff-prioritized service class. Fig.10(b) indicates that, with an increasing target


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We are implementing a more comprehensive service model with further considerations of application-level QoS requirements. More investigation of the packet-level QoS, such as the delay of packets, packet-loss probability, and their impact on the application-level QoS perceived by end users, will be carried out. REFERENCES

Fig. 10. Performance of the dynamic guard-channel scheme under a varying target handoff-dropping probability of the handoff-prioritized service class. (a) Handoff-dropping and (b) new-call-blocking probabilities.

handoff-dropping probability, the new-call-blocking probabilities of both real-time service classes tend to decrease slightly.

VI. CONCLUSION AND FUTURE WORK In this paper, we proposed an adaptive QoS management system in wireless multimedia networks. The proposed system is based on a service model designed for both connection- and application-level QoS. Wireless multimedia applications are classified into different service classes in the service model by their application profiles. Based on the service model, adaptive resource allocation is performed for each service class by employing the appropriate CAC and RR schemes tailored to the QoS requirements of the service class. Rate-adaptive multimedia applications can be incorporated for further improvement of the system performance. Through analysis and simulations, it was demonstrated that the proposed system can meet the QoS requirements of different service classes and achieve reasonably high network utilization.

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Lei Huang (M’04) received the B.S. and M.S. degrees from Beijing University of Posts and Telecommunications, Beijing, China, in 1993 and 1996, respectively, and the Ph.D. degree from the University of Southern California, Los Angeles, CA, in 2003, all in electrical engineering. Since August 2003, she has been an Assistant Professor in the Department of Electrical Engineering and Computer Science, Loyola Marymount University, Los Angeles, CA. Her research interests include digital image and video coding, multimedia applications in wired and wireless networks, quality of service, and network security.

Sunil Kumar (M’98) received the B.E. degree in electrical engineering from the National Institute of Technology, Surat, India, in 1988 and the M.E. and Ph.D. degrees in electrical and electronics engineering from the Birla Institute of Technology and Science, Pilani, India, in 1993 and 1997, respectively. From 1997 to 2001, he was a Postdoctoral Researcher and an Adjunct Faculty Member in the Department of Electrical Engineering—Systems, University of Southern California, Los Angeles. From 2000 to 2002, he also was a Senior Consultant in industry on MPEG-4 and JPEG2000 related projects and participated in JPEG2000 standards activities. Since 2002, he has been an Assistant Professor in the Department of Electrical and Computer Engineering, at Clarkson University, Potsdam, NY. He is the coauthor of a book and more than 50 technical publications in international conferences and journals. His research interests include robust image and video compression techniques and QoS-aware resource management in multimedia wireless and CATV networks.

C.-C. Jay Kuo (F’99) received the B.S. degree from the National Taiwan University, Taipei, in 1980 and the M.S. and Ph.D. degrees from the Massachusetts Institute of Technology, Cambridge, in 1985 and 1987, respectively, all in electrical engineering. He was a Computational and Applied Mathematics (CAM) Research Assistant Professor, Department of Mathematics, University of California, Los Angeles, from October 1987 to December 1988. Since January 1989, he has been with the Department of Electrical Engineering—Systems and the Signal and Image Processing Institute, University of Southern California, Los Angeles, where he currently has a joint appointment as Professor of both electrical engineering and mathematics. He has guided approximately 50 students to their Ph.D. degrees and has supervised ten postdoctoral research fellows. He is the coauthor of six books and more than 600 technical publications in international conferences and journals. His research interests are in the areas of digital signal and image processing, audio and video coding, multimedia communication technologies and delivery protocols, and embedded system design. Dr. Kuo is Editor-in-Chief for the Journal of Visual Communication and Image Representation, Associate Editor for IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING and Editor for the Journal of Information Science and Engineering and the RURASIP Journal of Applied Signal Processing. He is also on the editorial board of the IEEE SIGNAL PROCESSING MAGAZINE. He served as Associate Editor for IEEE TRANSACTIONS ON IMAGE PROCESSING from 1995 to 1998 and for IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY from1995 to 1997. He is a Fellow of SPIE and a Member of SIAM and ACM. He received the National Science Foundation Young Investigator Award (NYI) and the Presidential Faculty Fellow (PFF) Award in 1992 and 1993, respectively.