Computer Networks 53 (2009) 1087–1098

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Computer Networks

journal homepage: www.elsevier .com/ locate/comnet

A QoS-aware VoD resource sharing scheme for heterogeneous networks

Chenn-Jung Huang a,*, Kai-Wen Hu a, You-Jia Chen a, Chun-Hua Chen a, Yun-Cheng Luo b

a Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan, ROCb Institute of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, ROC

a r t i c l e i n f o a b s t r a c t

Article history:Received 22 June 2008Received in revised form 19 October 2008Accepted 19 December 2008Available online 29 December 2008

Responsible Editor: L. Jiang Xie

Keywords:Video-on-demandHeterogeneous networkQuality of serviceFuzzy logic

1389-1286/$ - see front matter � 2008 Elsevier B.Vdoi:10.1016/j.comnet.2008.12.014

* Corresponding author. Tel.: +886 38226738.E-mail address: cjhuang@mail.ndhu.edu.tw (C.-J.

In network-aware concept, applications are aware of network conditions and are adaptableto the varying environment to achieve acceptable and predictable performance. Two basicaspects of network-aware applications, network-awareness and network adaptation, havebeen widely addressed in the literature. In this work, a solution for video on demand ser-vice that integrates wireless and wired networks by using the network-aware concepts isproposed to reduce the blocking probability and dropping probability of mobile requests.Fuzzy logic inference system is employed to select appropriate cache relay nodes to cachepublished video streams and distribute them to different peers through service orientedarchitecture (SOA). SIP-based control protocol and IMS standard are adopted in this workto ensure the possibility of heterogeneous communication and provide a framework fordelivering real-time multimedia services over an IP-based network to ensure interoperabil-ity, roaming, and end-to-end session management. The experimental results demonstratethat effectiveness and practicability of the proposed work.

� 2008 Elsevier B.V. All rights reserved.

1. Introduction

Recent advances in communication and computer tech-nology have made increased the speed of transmissionacross the Internet. Owing to the improvements of com-puter technology, personal media devices such as mobileintelligent phones, portable PCs, mobile internet device(MID), and portable multimedia player (PMP), etc., allnow have multimedia capabilities and provide media cap-ture and playback as standard features. Moreover, therapid technological advances in broadband home connec-tivity [1,2], mobile wireless communications [3,4] and uni-versal mobile telecommunication system (UMTS) [5,6] aregetting matured and ubiquitous in recent years. Wired andwireless broadband access networks based on xDSL, WiFi,WiMAX, WCDMA or HSPA thus have increased people’sinterest in multimedia service over the Internet in recentyears. With the proliferation of high bandwidth networksand the rapid improvement of digital video technology,

. All rights reserved.

Huang).

various networked streaming media applications havebeen proposed in the recent years. Video-on-Demand(VoD) system has recently gained intensive considerationdue to its promising usage in a rich set of Internet-basedservices such as distance learning, rich media news distri-bution, and entertainment video distribution, etc.

With the increasing popularity of network-based multi-media applications poses many challenges for multimediacontent providers to provide efficient and scalable multi-media services. In the first, due to its big size and long-lived nature, multimedia streaming over Internet orwide-area wireless network needs significant amount ofnetwork bandwidth and server resources. Furthermore,the conventional design of VoD systems follows the Cli-ent–Server model, in which a set of centralized serversstore all the video files. In such a system implemented withClient–Server architecture, viewers have the flexibility ofspecifying both the video they want as well as the timeat which they wish to watch the video. A VoD system com-prises a video server with a video archive and several clientmachines linked via a local area network. Users request theirdesired videos using client software. The server delivers the

1088 C.-J. Huang et al. / Computer Networks 53 (2009) 1087–1098

requested video to the user in an isochronous data streamin response to a service request. Clients directly contactservers and request streaming content from servers. Obvi-ously, this architecture is not scalable since servers becomebottleneck as the requests increase. In addition, differentclients are likely to be asynchronously issuing requests toreceive their chosen media streams, this situation makesthe problem further complicated in order to efficiently dis-tribute VoD streaming media object across wide-areanetworks.

In the recent years, server load and network bandwidthare major performance issues in streaming video over theInternet. Resource sharing can significantly improve theperformance of VoD servers. Resource sharing strategiesinclude batching [7], patching [8], merging [9] and broad-casting [10,11]. A majority of existing VoD systems followsClient–Server model. However, this architecture is notscalable since servers will become the bottleneck as the re-quests increase. Several multimedia distribution tech-niques, such as mirroring, caching and contentdistribution [12–14], have been developed and deployedto ease the traffic load of the servers. In a proxy-basedarchitecture in which a set of proxies are deployed in thenetwork, clients can request the cached portion of videosfrom the proxies. Nevertheless, in both server-based andproxy-based architectures, servers and proxies are ex-pected to deliver high-quality streaming service to a largenumber of clients. Therefore, servers and proxies should bevery powerful in terms of computing power, outboundbandwidth, storage, etc., which makes the deploymentand maintenance cost highly expensive. On the other hand,recent research and experiments also reveal that currentInternet has enough resources to support large-scale mediastreaming in a peer-to-peer fashion [15–18].

It was reported that there are two major problemsneed to be addressed when we split published videosinto segments and distribute them to different peers ina heterogeneous peer-to-peer network: (1) How to dis-tribute and cache segments, taking into considerationthat peers offer different resources and may leave atany time. (2) How to efficiently find the desired segments[15,16]. In network-aware concept, applications areaware of network conditions and are adaptable to thevarying environment to achieve acceptable and predict-able performance. Moreover, as this kind of applicationapplies in the wired/wireless network environments dueto the different network characteristic such as dynamictopology and less bandwidth, the traditional video on de-mand service architecture needs to modify to address thewired/wireless characteristic. This motivates us to exploitthe often underutilized peers’ resources with network-aware concepts to support large-scale VoD services inthis work.

The basic idea of the proposed architecture is choosingappropriate cache relay nodes to cache published videostreams, distributing them to different peers through ser-vice oriented architecture (SOA), and using session initia-tion protocol (SIP) based control protocol [19] to ensurethe possibility of heterogeneous communication. IP Multi-media Subsystem (IMS) standard is adopted here becauseit is access network agnostic and provides a framework

for delivering real-time multimedia services over an IP-based network to ensure interoperability, roaming, andend-to-end session management. SIP-based infrastructureis assumed to be deployed in this work, in compliance withthe 3GPP’s IMS [20–22] standard. IMS is appealing becauseit provides a common service control infrastructure tyingdifferent heterogeneous and possibly complementary ac-cess network technologies together to build a reliableand robust communication network environment amongthe heterogeneous networks. IMS proposed a frameworkfor IP communication applications that deliver real-timemultimedia services across multiple networks and deviceswith a well-defined standard control layer. It separates theapplication layer from the core network over an IP-basednetwork to ensure interoperability, quality of service(QoS), end-to-end session management and a coherentframework for service charging. Users can access their sub-scribed services as long as the network access is providedvia a suitable IP core access network (IPCAN). Advancedservices can thus become easier to quickly developed anddeployed.

It is well-known that real-time streaming multimediaapplications such as voice over IP, online games and VoD,often require fixed bit rate and are delay sensitive. TheQoS guarantees for multimedia applications are especiallyimportant when networks resource is limited. The trans-mission overhead between a request node and a cache re-lay node is thus adopted as the essential parameter of theproposed VoD resource sharing scheme because it is animportant factor to ensure the QoS guarantees for thescheme. Notably, fuzzy logic inference system is employedin this work to select appropriate cache relay nodes tocache published video streams and distribute them to dif-ferent peers through SOA. The reason of using fuzzy logictechnique is that it has been used to solve several resourceassignment problems efficiently in ATM and wireless net-works in the literature [23]. It is expected that the applica-tion of fuzzy logic technique in this work can assist inbuilding a reliable and robust communication networkenvironment among the heterogeneous networks andsteadily transmitting video streaming. A series of experi-ments were conducted and the experimental results exhi-bit the feasibility of the proposed work.

The remainder of this paper is organized as follows. Sec-tion 2 presents the architecture of the scalable QoS-awareVoD resource sharing scheme in heterogeneous networks.The simulation results and analysis are given in Section3. Conclusion is made in Section 4.

2. Architecture of scalable QoS-aware VoD resourcesharing scheme for heterogeneous networks

In this work, the scalable QoS-aware VoD resource shar-ing scheme for heterogeneous network is proposed asshown in Fig. 1. This scheme is expected to build a reliableand robust communication network environment amongthe heterogeneous networks, including wired network,WiFi, WiMAX and traditional cellular network. IMS, SIPand SOA are adopted in this work to ensure the scalableVoD service in the heterogeneous environment. As shownin Fig. 1, the SOA architecture applied to the heterogeneous

Fig. 1. Architecture of scalable QoS-aware VoD resource sharing scheme for heterogeneous networks.

C.-J. Huang et al. / Computer Networks 53 (2009) 1087–1098 1089

network is composed of four portions, including service-provider, service-broker, service-requestor, and a resourcemanagement system. Acting as a service-provider, themultimedia content server and cache relay nodes publishand cancel the services to the cluster SIP servers, and pro-vide the multimedia data to the clients that have chosenthe providers. The cluster SIP servers, which act as the ser-vice-broker, search for services and integrate them byaccessing the service description in order to form the cacherelay node lists, and provide the lists to the clients whenthey request the service at the first time. Serving as the ser-vice-requestor, the clients will request for the service andthen receive the cache relay node list and multimediastreaming data from the cluster SIP servers and providers,respectively. The resource management system employedin this work consists of four modules, including cache relaynode list management, cache relay node selection module,cache relay node suitability calculation module, and band-width resource reserving mechanism. The main purpose ofthe resource management system proposed in this work isto avoid congestion and enhance the robustness of VoDservices via a suitable bandwidth reservation mechanismand the proper selection of cache relay nodes.

Three IMS layers including transport layer, controllayer, and service layer [24] are adopted in this work fordelivering real-time multimedia services over an IP-basednetwork. At control layer, the call session control function

(CSCF) module is the IMS core that handles all signalingmessages of the IMS terminals and lets the IMS terminalsconnect to the application devices including cluster SIPservers, multimedia content server, and cache relay node.The transport and control layers provide an integratedand standardized network platform that allows cache relaynodes and multimedia content server to offer various mul-timedia services at the service layer. Notably, three mod-ules of the resource management system employed inthis work, including cache relay node list management,cache relay node selection module, and cache relay nodesuitability calculation module, are operated at the threeIMS layers, while the bandwidth resource reserving mech-anism is operated at the transport layer.

Fig. 2 depicts the flow diagram of how the VoD resourcesharing scheme operates in heterogeneous network envi-ronment. When the client requests the video watching ser-vice, the system will request the cluster SIP servers forproviding the list of cache relay nodes. The list recordingall qualified cache relay nodes is published and integratedin the cluster SIP servers. Then the request node will sendthe query messages to all the cache relay nodes in the list.After receiving the confirmation message from the cacherelay nodes, the request node will select the most appro-priate cache relay node and then ask the chosen cache re-lay node to forward video streams. If no qualified cachenodes can be found in the list, the client will receive the vi-

Fig. 2. Flow diagram of how the VoD resource sharing scheme operates.

Fig. 3. Architecture of fuzzy cache relay node selection module.

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deo streams via an appropriate multimedia proxy serverallocated at the wired network instead. In case the streamchannels of the multimedia proxy server are all occupied,the request will wait for service before its preset timeout.After the client starts receiving the video streams fromthe cache relay node or the multimedia proxy, the requestnode will be asked to evaluate whether it is suitable toserve as a cache relay node, and notify its administratingSIP cluster server to update the cache relay node if it is aqualified candidate.

2.1. Resource management system

As illustrated in Fig. 1, the resource management sys-tem employed in this work consists of four modules,including cache relay node list management, cache relaynode selection module, cache relay node suitability calcu-lation module, and bandwidth resource reserving mecha-nism. The cache relay node list management is used tointegrate all clients’ information in the cluster SIP serversand then provide it to client for selecting the cache relaynode. After receiving the list, the client will attempt tospecify the proper cache relay nodes for delivering videostreaming by using the cache relay node selection module.Meanwhile, the client itself will employ cache relay nodesuitability calculation module to evaluate whether it isqualified to serve other clients. The bandwidth resourcereserving mechanism is adopted to reserve bandwidth re-

source at each base station in order to reduce the droppingprobability for the probable handoff requests.

2.1.1. Fuzzy cache relay node selection moduleIn order to reduce the control overhead, a two-stage

fuzzy cache relay node selection algorithm is employedto find the most appropriate relay nodes for forwardingVoD streams. The fuzzy logic techniques have been usedto solve several resource assignment problems efficientlyin ATM and wireless networks in the literature [23]. Thebasic function of each component in the fuzzy cache relaynode selection module as depicted in Fig. 3 is described asfollows:

� Fuzzifier: The fuzzifier performs the fuzzification func-tion that converts three inputs into suitable linguisticvalues which are needed in the inference engine.

� Fuzzy rule base: The fuzzy rule base is composed of a setof linguistic control rules and the attendant controlgoals.

� Inference Engine: The inference engine simulates humandecision-making based on the fuzzy control rules andthe related input linguistic parameters.

� Defuzzifier: The defuzzifier acquires the aggregated lin-guistic values from the inferred fuzzy control actionand generates a non-fuzzy control output, which repre-sents the predicted priority.

Fig. 4 shows the input–output mapping for the fuzzycache relay node selection module. The inputs to the fuzzylogic inference system include disconnection probability ofthe cache relay node, lifetime of the cache relay node, idlelevel of the cache relay node, and the transmission over-head. The output of the fuzzy logic system is the estimatedappropriateness level of the cache relay node. The appro-priateness level of each candidate cache relay node is col-lected by the request node to determine the mostappropriate cache relay node to forward the video streams.The fuzzy linguistic variables used for the output member-ship function are ‘‘low”, ‘‘medium” and ‘‘high”.

Fig. 4. Architecture of fuzzy cache relay node selection module.

2

2

2

1 x

e−

π

x

)(xP

Fig. 5. Idle level of each cache relay node.

C.-J. Huang et al. / Computer Networks 53 (2009) 1087–1098 1091

The disconnection probability of the cache relay noderepresents a prediction of the disconnection probabilityof a mobile entity as a cache relay node. The derivationof the disconnection probability will be discussed in Sec-tion 2.1.3. The lifetime of the cache relay node representsthe longest time period of the cache relay node that keepson-line in the historical records. The distribution of cacherelay node lifetimes in peer-to-peer environment can beexpressed by Pareto cumulative distribution function as gi-ven in [25,26].

FðxÞ ¼ 1� 1þ a � ðx� 0:5Þb

� ��1a

;a > 1; ð1Þ

where x represents the lifetime, a represents heavy-taileduser lifetimes and b stands for scale parameter to changethe mean of the distribution. Notably, two fuzzy linguistic

Lifetime Idel level

Rule r

Transmission ove

mediuhgihlow

Fig. 6. Reasoning procedure for Tsuk

variables ‘‘low” and ‘‘high” are used in the membershipfunctions for both the inputs, the cache relay node’s dis-connection probability and its lifetime.

The idle level of each cache relay node is determinedby,

PðxÞ ¼ 1ffiffiffiffiffiffiffi2pp e�

x22 ; ð2Þ

where x ¼ 1Nmax�Nused

. Nused denotes the counts of the mobileentity currently serving as the cache relay nodes, whereasNmax represents the upper bound that a mobile entity canserve as the cache relay nodes. Nmax is determined by,

Nmax ¼Bmax

Bavg; ð3Þ

where Bmax denotes the maximum bandwidth that thecache relay node can offer, and Bavg is the average band-width that a request node needs for video watching. Twofuzzy linguistic variables ‘‘low” and ‘‘high” are used inthe membership functions for the idle level of the cache re-lay node when it is used as one of the inputs to the fuzzylogic system. Fig. 5 depicts the relationship between theidle level and the variable x used in Eq. (2).

The transmission overhead between the request nodeand cache relay node is adopted as another input to thefuzzy logic system. Three linguistic term sets, ‘‘small”,‘‘medium” and ‘‘large” are used for this input.

Fig. 6 illustrates the reasoning procedure. The rule as gi-ven in [27] is:

IF the cache relay node’s lifetime is ‘‘low”, AND its idlelevel is ‘‘high”,ANDthe transmission overhead between thecache relay node and the request node is ‘‘medium”, ANDthe disconnection probability of the cache relay node is‘‘low”,

THEN the estimated appropriateness level of the cacherelay node is ‘‘medium”.

The non-fuzzy output of the defuzzifier can then be ex-pressed as the weighted average of each rule’s output afterthe Tsukamoto defuzzification method is applied:

P ¼P27

i¼1Pi �wiP27i¼1wi

; ð4Þ

where Pi denotes the output of each rule induced by the fir-ing strength wi. Notably, wi represents the degree to whichthe antecedent part of each fuzzy rule constructed by theconnective ‘‘AND” as shown in the above example issatisfied.

Disconnection probability

Minimal

Appropriateness levelrhead

m wol medium

amoto defuzzification method.

Fig. 7. Computation of suitability of the request node that serves as acache relay node.

Fig. 8. Probability of the mobile entity’s moving direction determined bynormal distribution.

θ angleπ3

2-

2π

( )PQA

2π

θ

Fig. 9. Probability of the mobile entity’s moving direction determined byskewness distribution.

1092 C.-J. Huang et al. / Computer Networks 53 (2009) 1087–1098

2.1.2. Estimation of request node’s suitability to serve as acache relay node

As shown in Fig. 7, fuzzy logic inference system isadopted to determine the suitability of the request nodethat serves as a cache relay node. The buffer size of the re-quest node, its computing capacity specified in terms ofCPU clock speed and its disconnection probability are usedas the three inputs to the fuzzy logic system. The outputparameter of the inference engine is the estimated suitabil-ity of the request node. Notably, the prediction of requestnode’ stability will be explained in Section 2.1.3, and threefuzzy linguistic variables, ‘‘low”, ‘‘medium” and ‘‘high”,give different levels in the membership functions for thethree inputs and the output parameters.

2.1.3. Prediction of mobile entities’ disconnection probabilityThe predictions of the disconnection probability of the

cache relay node and that of the request node are expectedas one essential input to the two fuzzy logic systems as dis-cussed in Sections 2.1.1 and 2.1.2, respectively. Derivationof the prediction of the mobile entities’ disconnectionprobability is discussed as follows.

2.1.3.1. Probability distribution. This work assumes thateach mobile entity is assigned a built-in positioningsystem, such as global positioning system (GPS), or WiFipositioning system (WPS), in order to obtain speed andlocation information of the mobile entity. It was reportedthat WPS is able to get the position and direction informa-tion more quickly and accurately then GPS [28,29] by usingthe MAC addresses of nearby wireless access points andproprietary algorithms, especially in indoor and urbanarea. Thus, many industrial products, such as GPS intelli-gent phone [30], GPS PDA [31], iPhone system [32], mobileInternet device (MID) [33], and Skyhook Wireless [34],have been making use of GPS or WPS for positioningpurpose.

Meanwhile, the direction of mobile entity is assumed tobe influenced mainly by its current speed vn and its currentacceleration a. Three cases are accordingly considered inorder to forecast the direction of the mobile entity at thenext time period.

Case 1: When a > 0, it implies that the mobile entityspeeds up its movement. The mobile entity has higherprobability to keep moving in the same direction than tochange direction. The probability of moving to each direc-tion is determined by normal distribution as shown inFig. 8. The probability distribution function (PDF) is givenby:

PQA! ðhÞ ¼

R h�p2

1ffiffiffiffi2pp

r e�x�p

2ð Þ2

2r2 dx if � p2 6 h 6 p

2

R 3p2

h1ffiffiffiffi

2pp

r e�x�p2ð Þ

2

2r2 dx if p2 6 h 6 3p

2

8>>><>>>:

; ð5Þ

where h is the complementary angle between MH’s currentdirection and its direction at next time period as illustratedin Fig. 8, and r is the variance of h.

Case 2: When a < 0, it implies that the mobile entityslows down, and the mobile entity has higher probabilityto change direction instead of keeping the same directionduring the next period of time. The probability of eachmoving direction is determined by Skewness distributionas shown in Fig. 9, and the PDF is given by

PQA! ðhÞ ¼

R h�p2

1ffiffiffiffi2pp

r e�x�p

2�1aj jð Þ2

2r2 dx if h < p2 � 1

a

�� ��1�

R h�p2

1ffiffiffiffi2pp

r e�x�p2�

1aj jð Þ2

2r2 dx if h > p2 � 1

a

�� ��R h�p2

1ffiffiffiffi2pp

r e�x�p

2� 1þ1aj jð Þ2

2r2 dx if h < p2 � 1þ 1

a

�� ��1�

R h�p2

1ffiffiffiffi2pp

r e�x�p2� 1þ1

aj jð Þ22r2 dx if h > p

2 � 1þ 1a

�� ��

8>>>>>>>>>>>>><>>>>>>>>>>>>>:

;

ð6Þ

where is the complementary angle between MH’s currentdirection and its direction at next time period, r denotesthe variance of h, and a is the acceleration rate.

Case 3: When vn = 0, it implies that the mobile entitystops moving, the probability of each direction should beidentical. The probability of the mobile entity’s movingdirection is determined by Uniform distribution as illus-trated in Fig. 10, and the PDF is given by:

PQA! ðhÞ ¼ 1

360; ð7Þ

Fig. 10. Probability of the mobile entity’s moving direction determinedby uniform distribution.

C.-J. Huang et al. / Computer Networks 53 (2009) 1087–1098 1093

2.1.3.2. Position forecast. The forecast of the mobile entity’sposition at the next time period is used to derive the dis-connection probability in this work. The location of themobile entity is first predicted by using the mobile entity’scurrent speed, vn and the probability distribution derivedin the preceding subsection,

dðhÞ ¼ vn � t þ 12

at2� �

� 1þ sin h2

� �� PðhÞ; ð8Þ

where vn is the mobile entity’s current speed, t denotes thespanned time intervals, a is the acceleration, and h is theangle between the normal of vn and the mobile entity’snext moving direction.

One example that corresponds to Case 1 discussed inthe preceding subsection is given in Fig. 11. Notably, themaximal distance that the mobile entity can move duringthe next time period is encircled by a thick line in theexample. Dot Q in the example designates the current loca-tion of the mobile entity.

BS

APAP

AP

WiMAX

WiFi

WiFi

Wi

Q

B

CD

E

F

G

b1h1

b2

h2

θ1

θ2

Fig. 11. Maximal distance that the mobile entity can move during the next timea = 1.

2.1.3.3. Disconnection forecast. The disconnection probabil-ity is derived based on the location of the mobile entity atthe next time period. Take Fig. 11 as an example. The totalarea encircled by the thick line in the figure represents thepossible location that the mobile entity can reach at thenext time period. The area can be computed by,

Areatotal ¼Z 2p

0dðhÞdh: ð9Þ

As illustrated in Fig. 11, the mobile entity that is currentlylocated at dot Q will disconnect with the three WiFis whenthe mobile entity reaches the area that is not covered bythe three WiFis. Notably, when a candidate cache relaynode needs to switch between two different types of net-works as shown in Fig. 11, a disconnection from its currentresiding network is assumed in this work to avoid an unex-pected service disruption owing to vertical handovercaused by the roaming of the cache relay node within theoverlapped coverage of heterogeneous networks [35,36].

To simplify the computation, we approximate the areasof BCD and EFG in Fig. 11 by two triangles. Then the areathat the mobile entity will disconnect with the three WiFisin the example is given by,

Areano signal ¼Z h1þh2

h1

dðhÞdh�Z h1þh2

h1

rdh

� �

þ 12

Xn

i¼1

bi � hi

!; ð10Þ

where d(h) is predicted location of the mobile entity as gi-ven by Eq. (8), and r is the distance between the currentlocation of mobile entity and the power boundary of the

BSCellular

Fi

BSCellular

period in an example of Case 1 in Section 2.1.3.1, in which v = 16m/s and

1094 C.-J. Huang et al. / Computer Networks 53 (2009) 1087–1098

WiFi that the mobile entity resides, and bi and hi denote thebase, and the height of each triangle, respectively.

Accordingly, the disconnection probability of the mo-bile entity at the next time period can be derived by,

S ¼ PðAreano signalÞ ¼Areano signal

Areatotal

¼

R h1þh2h1

dðhÞdh�R h1þh2

h1r dh

� �þ 1

2

P4i¼1bi � hi

� �R 2p

0 dðhÞdh; ð11Þ

Table 1Simulation parameters.

Parameter type Parameter value

Simulation time 24 hSimulation terrain 20 km � 20 kmNumber of video title 200Video length 120 minChannels of multimedia proxy 1000Mobility model Attractor-point-based

mobility modelMobility 0 � 20 m/sWired network types EthernetWireless network types WiFi/WiMAX/CellularWireless network Mac protocol 802.11/802.16/HSUPATransmission range 100 m/10 km/6 kmRatio of BS (APs) for three wireless

network types4:1:2

2.1.4. Adaptive bandwidth resource reserving mechanismIn order to reduce the dropping probability for the

handoffs, this work proposes an adaptive bandwidthreserving mechanism to reserve bandwidth resource ateach base station for the clients which might handoff fromneighboring base stations. Since the proposed scheme re-serves an appropriate amount of bandwidth for the ex-pected incoming clients during the next time period,determines the amount of reserved bandwidth for the cli-ents handoff from other base station during the next timeperiod from the current network traffic load using Eq. (12),

Rrðt þ 1Þ ¼ K � Bw � dðtÞ � n̂Aðt þ 1Þ; ð12Þ

where Bw denotes the total bandwidth of the base station;d is the dropping probability during the current time peri-od; n̂Að�Þ represent the predicted numbers of clients hand-off from other base station during the next time period,respectively, and K is a constant less than 1. Notably, thevalues of n̂Að�Þ is predicted by the weighted moving aver-age method.

2.1.4.1. Weighted moving average method. Since n̂Að�Þ canboth be considered as time series, this work predicts thevalue of the next time interval using a well-known timeseries predictor, the weighted moving average method[37], which has been reported to perform well on time ser-ies prediction [38]. A time series is a sequence of numericalvalues indexed by increasing time unit. The conventionallyadopted time series prediction techniques in the literatureinclude the ‘average’ method, which determines the meanof the past m measurement periods:

�k ¼Pm�1

j¼0 kðt � jÞm

; ð13Þ

and the weighted moving average, which increases theweight of the last measurement period,

k̂ðt þ 1Þ ¼ ð1� qÞ � �kþ q � kðtÞ; ð14Þ

where q = 1, and �k represents the average calculated in Eq.(13).

3. Simulation results and analysis

We ran a series of simulations to examine the effective-ness and the feasibility of the proposed scheme. It is as-sumed that the system contains 200 videos, each is120 min long and the relative frequency of the individualvideo is stretched exponential distribution. Yu et al. [39]and Veloso et al. [40] analyzed a wide variety of media

workloads on the Internet. They accumulated the work-loads from both the client and the server sides in theWeb, VoD, P2P, and live streaming environments between1998 and 2006. The media content was delivered via Web/P2P downloading or unicast/multicast streaming. BothVeloso et al. [40] and Sripanidkulchai et al. [41] reportedthat live streaming media systems follow stretched expo-nential (SE) distribution. The SE distribution lays out ananalytical foundation to establish scheduling methods fordelivering the rapidly increasing quantity of Internet med-ia content. Therefore, this study assumed that the VoD sys-tem frequently updates contents in our simulationscenarios, matching the characteristics of a SE distribution.

The detailed simulation parameters are shown in Table1. The simulation environment is a 20 � 20 square kilome-ters, and the mobile nodes are randomly distributed inwireless heterogeneous networks. In accordance with themobility model [42], the users can move without limita-tions inside the whole scenario according to a randomwalk model with the velocity and acceleration of each nodemoves range from 0 m/s to 15 m/s and from 0 m/s2 to 2 m/s2, respectively. The total simulation time for each run is24 h. Network types in the simulation are divided into fivetypes, including wired, WiFi, WiMAX and Cellular net-works. Transmission range of WiFi is 100 m and that of Wi-MAX and Cellular is 10 km and 6 km, respectively. Thenumber of WiMAX base stations, Cellular base stations,and WiFi access points is 20, 40, and 80, respectively.

3.1. Performance metrics

In the analysis of our resource sharing and schedulingpolicies, the following performance measures are identicalto the ones adopted in [43]:

� Blocking probability: This is the probability that the clientleaves the system without being serviced due to the lackof server stream.

� Dropping probability: This is the probability that the cli-ent leaves the system due to the lack of bandwidthresource when the client handoffs to another basestation.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

50 100 150 200 250Client Arrival Rate(Requests/Minute)

Dro

ppin

g P

roba

bili

ty

LMVoD UVoD FSSVoD SSVoD

Fig. 13. Dropping probability for the three compared schemes undervaried client arrival rates.

0

2

4

6

8

10

12

14

16

50 100 150 200 250

Client Arrival Rate(Requests/Minute)

Ave

rage

Lat

ency

LMVoD UVoD FSSVoD SSVoD

Fig. 14. Average latency for the three compared schemes under variedclient arrival rates.

11001200130014001500

ut

LMVoD UVoD FSSVoD SSVoD

C.-J. Huang et al. / Computer Networks 53 (2009) 1087–1098 1095

� Average latency time: The latency of a client is the periodwhich elapses between the arrival of the video requestand the time when the service to the display device isactually initiated. Only non-blocking clients are consid-ered in the latency time measure.

� Throughput: Transmission throughput is the maximumamount of video streaming transmitted to clientsthrough base stations.

3.2. Simulation results

We first ran a series of experiments wherein arrival ratewas varied from 50 to 250 requests per minute with thenumber of BSs and APs fixed at 1400. Then we varied thenumber of BSs and APs from 1400 to 7000 while fixing ar-rival rate at 200 requests per minute. The comparedschemes include the proposed VoD resource sharingscheme (FSSVoD), the pure SIP-based SOA VoD resourcesharing scheme (SSVoD), the primitive unicast VoD re-source sharing scheme (UVoD) and a state-of-the-art mul-ticast scheme in the literature, LAGRANGE-based multicastVoD resource sharing scheme (LMVoD) [27,44].

LMVoD [27,44] selects the cells and the wireless tech-nologies for layer-encoded video multicasting in heteroge-neous wireless networks. This mechanism formulated Celland Technology Selection Problem (CTSP) in the heteroge-neous wireless networks as an optimization problem.Notably, the purpose of solving the CTSP was to selectthe cell and the wireless technology for each group mem-ber to minimize the total bandwidth cost of the shortestpath tree under current wireless network infrastructures.The mechanism employed LAGRANGE-based on Lagran-gean relaxation on the integer linear programming formu-lation iteratively converging toward the optimal solutions.It was observed that algorithm is adaptive to the change inthe subscribers at each layer of video and the change of thelocation of each mobile entity.

Figs. 12–15 show the comparisons of blocking probabil-ity, dropping probability, average latency time andthroughput, respectively. It can be observed that the block-

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

50 100 150 200 250

Client Arrivale Rate(Requests/Minute)

Blo

ckin

gP

roba

bili

ty

LMVoD UVoD FSSVoD SSVoD

Fig. 12. Blocking probability for the three compared schemes undervaried client arrival rates.

50 100 150 200 2500

100200300400500600700800900

1000

Client Arrival Rate(Requests/Minute)

Thr

ough

p

Fig. 15. Throughput for the three compared schemes under varied clientarrival rates.

ing probability and the average latency of the proposedFSSVoD scheme are smallest, whereas the throughput isthe highest. The multimedia proxy server becomes the bot-tleneck in the primitive unicast and LAGRANGE-basedmulticast VoD resource sharing schemes because the videostreams are delivered merely by the multimedia proxy ser-ver in these two schemes. The later requesters will tend to

0

2

4

6

8

10

12

14

16

1400 2800 4200 5600 7000Number of BSs and APs

Ave

rage

Lat

ency

LMVoD UVoD FSSVoD SSVoD

Fig. 18. Average latency for the three compared schemes under fixedclient arrival rate (200 requests per minute).

15002000250030003500400045005000

Thr

ough

put

(Mbp

s/M

inut

e)

LMVoD UVoD FSSVoD SSVoD

1096 C.-J. Huang et al. / Computer Networks 53 (2009) 1087–1098

leave the VoD system owing to their impatience when thebandwidth resources of multimedia proxy server in thesetwo schemes are all occupied by early arrival.

Fig. 13 shows that the dropping probability of FSSVoDscheme is smaller than those of LMVoD and SSVoD. Thedropping probability of pure unicast scheme is the lowestbecause more new arriving clients under UVoD schemeare all blocked as exhibit in Fig. 12 and more bandwidthof each base station in UVoD scheme can be assigned tothe clients that handoff from neighboring base stations.Fig. 14 shows that the average latency of our proposed FSS-VoD scheme is lower than those of the other threeschemes. The average latency of the pure unicast VoD re-source sharing scheme is much higher than the othertwo schemes because the unicast scheme has to providea channel to serve each individual requester and the lim-ited bandwidth is easier to be exhausted than the othertwo schemes.

Next we show the results of varying the number of BSsand APs from 1400 to 7000 while fixing the arrival rate ofthe requests and the bandwidth of multimedia proxy. Asgiven in Figs. 16–19, the blocking probability, average la-tency and throughput of FSSVoD still outperform the otherthree schemes owing to the efficient SOA structure and theappropriate choice of the cache relay nodes.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Number of BSs and APs

Blo

ckin

g P

roba

bili

ty

LMVoD UVoD FSSVoD SSVoD

1400 2800 4200 5600 7000

Fig. 16. Blocking probability for the three compared schemes under fixedclient arrival rate (200 requests per minute).

00.10.20.30.40.50.60.70.80.9

1

1400 2800 4200 5600 7000Number of BSs and APs

Dro

ppin

g P

roba

bili

ty

LMVoD UVoD FSSVoD SSVoD

Fig. 17. Dropping probability for the three compared schemes underfixed client arrival rate (200 requests per minute).

1400 2800 4200 5600 70000

5001000

Number of BSs and APs

Fig. 19. Throughput for the three compared schemes under fixed clientarrival rate (200 requests per minute).

Under primitive VoD (UVoD) scheme, the later requestswill tend to leave the VoD system owing to their impa-tience when the bandwidth of multimedia proxy serverin the scheme is all occupied by early arrival. Therefore,the blocking probability, average latency and throughputof UVoD are the poorest. On the other hand, the droppingprobability of UVoD turns out to be the lowest becauseeach base station can provide sufficient bandwidth forhandoffs owing to the high blocking probability for thenew arriving requests.

4. Conclusions

In this work, a solution for video on demand service thatintegrates wireless and wired networks by using the net-work-aware concepts is proposed to reduce the blockingprobability and dropping probability of mobile requests.Fuzzy logic inference system is employed to select appro-priate cache relay nodes to cache published video streamsand distribute them to different peers through service ori-ented architecture (SOA). Notably, the prediction of mobileentities’ stability that is required as one essential input tothe fuzzy logic system is also derived. SIP-based controlprotocol and IMS standard are adopted in this work to en-sure the possibility of heterogeneous communication andprovide a framework for delivering real-time multimediaservices over an IP-based network to ensure interoperabil-

C.-J. Huang et al. / Computer Networks 53 (2009) 1087–1098 1097

ity, roaming, and end-to-end session management. A seriesof simulations was run to compare the proposed VoD re-source sharing schemes. The experimental results demon-strate that effectiveness and practicability of the proposedwork in terms of the performance metrics, including block-ing probability, dropping probability and average latencydelay.

Acknowledgements

The authors would like to thank the National ScienceCouncil of the Republic of China, Taiwan for financiallysupporting this research under Contract Numbers NSC96-2628-E-259-022-MY3 and NSC 97-2218-E-259-005.

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Chenn-Jung Huang received the B.S. degreein electrical engineering from National Tai-wan University, Taiwan and the M.S. degree incomputer science from University of SouthernCalifornia, Los Angeles, in 1984 and 1987. Hereceived the Ph. D degree in electrical engi-neering from National Sun Yat-Sen University,Taiwan, in 2000. He is currently a Professor inthe Department of Computer and InformationScience, National Dong Hwa University, Tai-wan. His research interests include computercommunication networks, data mining, and

diagnosis agent for e-learning.

Kai-Wen Hu is pursuing a Master’s degree atthe Institute of Learning Technology, NationalDong Hwa University, Taiwan. His researchinterests include computer communicationnetworks, data mining and applications ofmachine learning techniques.

You-Jia Chen is pursuing a Master’s degree atthe Institute of Learning Technology, National

Dong Hwa University, Taiwan. His researchinterests include computer communicationnetworks, data mining and applications ofmachine learning techniques.

Chun-Hua Chen is pursuing a Master’s degreeat the Institute of Learning Technology,National Dong Hwa University, Taiwan. Hisresearch interests include computer commu-nication networks, data mining, applicationsof machine learning techniques and e-learning.

Yun-Cheng Luo received Master’s degree inthe Institute of Learning Technology, National

Dong Hwa University, Taiwan, 2008. Hisresearch interests include computer commu-nication networks, data mining, and applica-tions of machine learning techniques.