Research Article A Fairness-Based Access Control Scheme to ...downloads.hindawi.com/journals/mpe/2014/207402.pdfResearch Article A Fairness-Based Access Control Scheme to Optimize

Research ArticleA Fairness-Based Access Control Scheme to Optimize IPTV FastChannel Changing

Junyu Lai,1 James C. F. Li,1 Alireza Abdollahpouri,2 Jianhua Zhang,3 and Ming Lei1

1 NEC Labs China, Beijing 10084, China2Department of Computer Engineering and Information Technology, University of Kurdistan, Sanandaj 66177 15175, Iran3 College of Computer Science, Zhejiang University of Technology, Hangzhou 310023, China

Correspondence should be addressed to Junyu Lai; [email protected]

Received 2 April 2013; Revised 11 November 2013; Accepted 26 November 2013; Published 4 February 2014

Academic Editor: Dongdong Ge

Copyright © 2014 Junyu Lai et al.This is an open access article distributed under theCreative CommonsAttribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

IPTV services are typically featured with a longer channel changing delay compared to the conventional TV systems. The majorcontributor to this lies in the time spent on intraframe (I-frame) acquisition during channel changing. Currently, most widelyadopted fast channel changing (FCC) methods rely on promptly transmitting to the client (conducting the channel changing)a retained I-frame of the targeted channel as a separate unicasting stream. However, this I-frame acceleration mechanism hasan inherent scalability problem due to the explosions of channel changing requests during commercial breaks. In this paper, wepropose a fairness-based admission control (FAC) scheme for the original I-frame accelerationmechanism to enhance its scalabilityby decreasing the bandwidth demands. Based on the channel changing history of every client, the FAC scheme can intelligentlydecide whether or not to conduct the I-frame acceleration for each channel change request. Comprehensive simulation experimentsdemonstrate the potential of our proposed FAC scheme to effectively optimize the scalability of the I-frame accelerationmechanism,particularly in commercial breaks. Meanwhile, the FAC scheme only slightly increases the average channel changing delay bytemporarily disabling FCC (i.e., I-frame acceleration) for the clients who are addicted to frequent channel zapping.

1. Introduction

A vitally important factor leading IPTV services to besuccessful is to ensure clients’ quality of experience (QoE) tobe better than or at least comparable to the conventional TVsystems. However, IPTV systems are generally featured witha longer channel changing delay, which is around 2 seconds,and can lengthen from 5 to 10 seconds with next generationvideo coding paradigms.This unfavourable feature can fatallydegrade clients’ QoE and, consequently, incur revenue lossfor IPTV service providers. TV channel changing delay isdefined as the latency between the initiation of changingto a targeted channel (e.g., by pressing the buttons on theremote control) and the final display of the content onthat channel. In a typical IPTV system, channel changingdelay consists of several different components, which aresummarized in Table 1. A representative mean value for eachcomponent is also given based on previous literature andpractical measurements [1]. It is obvious that the latency

owing to Intraframe (I-frame) acquisition is the dominantfactor contributing to the total channel changing delay. I-frame acquisition delay is directly related to the compressionschemes used in modern video codec technologies (such asMPEG-2 or MPEG-4). More specifically, pictures in a videostream are encoded into either intracodedmode (i.e., I-frame)or interprediction mode (i.e., P- or B-frame). I-frames do notrely on any other frame for decoding, while the decoding of aP- or B-frame depends on the neighbouring frame(s). Owingto the fact that the size of an I-frame is typically much largerthan that of a P- or B-frame, the frequency of the I-framesin a video stream should be small enough for an efficientcompression scheme.Also note thatwhen a user switches intoa specific channel the upcoming I-frame should be the firstindependently decodable frame. Thus, we denote the timea user has to wait until the next I-frame arrives as I-frameacquisition delay.

In order to decrease the IPTV channel changing delay,various fast channel changing (FCC) methods are proposed.

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2014, Article ID 207402, 12 pageshttp://dx.doi.org/10.1155/2014/207402

2 Mathematical Problems in Engineering

Table 1: Major components of channel changing delay.

Delay component Typical latency (ms)IGMP session joining/leaving formulticast delivery ∼100

Network latency and jitter ∼100Client stream buffering (network dejitterbuffer and video input buffer) ∼250

I-frame acquisition ∼1000 (GOP durationis 2 seconds)

Video/audio synchronization ∼200Content authorization ∼100

Among them the most widely adopted ones in practical engi-neering are based on the so-called I-frame acceleration mech-anism. The IPTV system promptly transmits to a user con-ducting channel changing a retained I-frame of the requestedchannel as an (accelerated) uncasting communication, so asto reduce user waiting time for the first picture of the targetedchannel. However, thismechanismhas an inherent scalabilityproblem which can be aggravated by the explosions of chan-nel changing requests during commercial breaks. In particu-lar, the scalability problem refers to the case that the networkbandwidth required by using this I-frame acceleration mech-anism can be considerably larger than the available band-width, which accordingly introduces heavy degradations tonetwork quality of service (QoS) and user QoE. In order tomitigate the problem, we propose a fairness-based admissioncontrol (FAC) scheme as a scalability optimization methodfor the I-frame acceleration mechanism. Based on the chan-nel changing histories of all the clients, our proposed FACscheme can intelligently decide whether or not to conduct I-frame acceleration at each arrival of channel change requests.We carry out comprehensive simulation experiments, whichdemonstrates the potential of our proposed FAC schemeto optimize the scalability of the most widely used I-frameacceleration mechanism. Besides, the disadvantage broughtby using the FAC scheme is also investigated.

The rest of the paper is organized as follows. Section 2presents the state-of-the-art. More detailed introduction ofthe I-frame acceleration mechanism as well as its shortcom-ings is introduced in Section 3. Our major contribution isillustrated in Section 4, where we propose a FAC scheme inorder to optimize the scalability of the aforementioned I-frame acceleration mechanism. Comprehensive simulationexperiments have been conducted to evaluate the perfor-mance of the FAC scheme in Section 5. Finally, summary andoutlook are given in Section 6.

2. Related Work

As aforementioned, a short channel changing delay is oneof the key factors for IPTV services. In both academiaand industry, there exist a plenty of research efforts toreduce the delay associated with different parts of channelchanging process (such as those listed in Table 1). In [2],Cho et al. proposed an approach based on the prediction that

the adjacent channels are more likely to be watched byviewers (choosing channel by UP and DOWNbuttons on theremote control) for the next time of channel changing. Theirmethod requires to conduct the simultaneous transmissionof the two adjacent channels. Soon after that, an improvedapproachwas introduced in [3], where a statistical selection ofchannels are derived based on the historical channel changinginformation of each user, which is more accurate than alwayschoosing the adjacent channels. However, the disadvantagelies in the fact that the bandwidth demand is proportionalto the number of channels being concurrently transmittedand, consequently, the bandwidth demands will be tripledif these methods are applied. In order to mitigate the delayintroduced by IP multicasting, [4] developed the source-specific multicast (SSM) mechanism to significantly reducethe complexity of IP multicasting routing process. Thereare a set of schemes [5, 6] aiming to decrease the I-frameacquisition latency based on adding a channel change tune-instream, in addition to the main stream, for each provided TVchannel. More specifically, the tune-in stream can be either alow-resolution (I-frame only) stream [5] or a full resolution(I-frame only) stream [6]. However, the former scheme canonly provide a lower resolution before a full resolution I-frame arrives on the main stream, and the latter scheme mayintroduce prediction mismatches for the P- or B-frames inthe main stream if the prediction is based on an I-framein the tune-in stream. Similar to the low-resolution tune-instream scheme, scalable video coding (SVC) [7] technologycan also be employed to reduce the channel changing delay byusing the base layer as the tune-in stream.The only differencelies in the fact that, after turning to the enhancement layerstream, the base layer stream is still used together with theenhancement layer stream to produce a full resolution video.In practice, the most widely used methods (e.g., Microsoft’sFCC [8]) are based on the I-frame acceleration mechanism.As alreadymentioned, thismechanism promptly transmits toa user conducting channel changing a retained I-frame of therequested channel, aiming to reduce the latency for displayingthe first picture of the targeted channel. This method isbased on IP unicasting, which may consume a large amountof network bandwidth. In order to mitigate this problem,IP multicasting based I-frame acceleration mechanism wasproposed in [9]. As a summary, each of the above approacheshas its advantages and application limits. In general, thereexist trade-offs between the considered channel changingdelay and some other important performance metrics, forexample, the consumed bandwidth.

As opposed to conventional TV systems, IPTV userbehaviour can directly influence the traffic loads on the trans-mission networks. A large amount of existing research effortshave been conducted on the IPTV traffic characterization aswell as user behaviourmeasurement, analysis, andmodelling.In [10] Qiu et al. studied a large national IPTV system anddeveloped a series of analytical models for different aspectsof user activities. Ramos et al. [11] modelled the behaviourof a typical user as a rapid burst of channel selection eventsfollowed by an extended period of channel viewing. Yu et al.[12] investigated IPTV user activities in terms of zapping rateand session lengths. Cha et al. [13] studied how users select

Mathematical Problems in Engineering 3

Access Node

Access Node

Access Node

Client

Client

Client

Client

VideoSource

Client

Client

(1)

(2)

Network(multicasting and unicasting)

(1) Channel request from a client (2) Multicast the requested channel

Figure 1: A typical IPTV distribution architecture.

channels in the real world based on a comprehensive tracefrom a commercial IPTV service, including channel popular-ity dynamics, aggregate viewing sessions, and content locality.In our previous work [14, 15] (which was an extension ofearlier IPTV user behaviour models), we modelled the chan-nel switching behaviour of a single IPTV user. Both channelpopularity and user activities have been taken into account.Our proposed IPTV user behaviour automaton (IPTV-UBA)model relies on formal descriptions to capture the character-istics of user behaviours.The aggregate user behaviour can bederived by overlaying the behaviours of a set of users.

3. The Most Widely Used Fast ChannelChanging Mechanism and Its Shortcomings

As summarized in the previous section, a plenty of researchefforts have been carried out to improve the QoE for IPTVclients by shortening the channel changing delay from differ-ent perspectives regarding the factors listed in Table 1. Dueto the fact that I-frame acquisition delay usually has thelargest contribution to the total delay, the most effective andpractically deployed fast channel changingmethods are basedon the I-frame acceleration mechanism. This mechanismwas firstly proposed by Microsoft [8] and soon afterwardsother companies (such asHuawei, ZTE, andCisco) publishedtheir own improvements. In this section, we first present theprinciple behind the I-frame acceleration mechanism. Then,we discuss the shortcomings of using this mechanism andanalyse the reasons.

3.1. I-frame Acceleration Mechanism. In a typical IPTV dis-tribution architecture as illustrated in Figure 1, there existsa Video Source responsible for transmitting all the live TVchannels to its downstream Clients by means of dynamic orstatic multicasting via the Network in between. The networkin the architecture contains a plurality of replication nodes(such as aggregation routers and switches), which forms ahierarchical networking architecture (e.g., tree topology) andsupports both multicasting and unicasting communications.The simplified process of a user carrying out a new channelchange is marked in Figure 1. A client in the first step sendsa channel change request to the Video Source. After the

request is approved, the Video Source starts to multicastthe requested channel to the client in the second step.In fact, the multicasting joining process corresponding tothe channel changing can usually be accomplished by theclosest intermediate node to the client in the transmissionpath, where the requested channel is already available. It isworth noting that in this scenario all the TV channels aretransmitted by means of IP multicasting, and the channelchanging delay due to I-frame acquisition can be considerablylarge and variable as discussed in Section 1.

In order to reduce the channel changing delay andimprove the QoE for clients by applying the I-frame accelera-tionmechanism, a channel change server (CCS) is introducedin the architecture presented in Figure 1 (see Figure 2).Depending on various system implementation features suchas the scale of an IPTV system, the location of a CCS can beeither at the edge of the core network or within a point-of-presence (PoP). The CCS is configured to retain at least themost recent I-frame, that is, the one just transmitted beforethe current instant, for each IPTV channel, and is adaptedto respond to channel change requests from clients bytransmitting the retained I-frame of a requested TV channelusing an IP unicasting communication (probably with ahigher transmission bit-rate than the average multicastingbit-rate), so that clients can receive the first picture of therequested channel much faster. Figure 2 demonstrates theservice procedure when a client conducts the channel chang-ing with applying the aforementioned I-frame accelerationmechanism. As can be seen, in the first step, a channelchange request from a client is directly sent to the CCS.Then,upon receiving the request the CCS immediately transmitsthe retained I-frame of the requested channel back to theclient by means of IP unicasting. Next, the client initiatesthe multicasting joining process to the requested TV channeltowards the Video Source. At last, if successfully joining intothe multicasting group, the client starts to receive the IPmulticasting flow of the requested channel.

3.2.The Shortcomings of the I-Frame AccelerationMechanism.It can be observed from practical IPTV systems that channelchanging events are usually correlated and aligned with thetimeline of TV programs. More specifically, at the end ofeach TV program (or during the commercial breaks), a large


Client

Client

Client

Client

Client

Client

(1) Channel request from a client

(3) Conduct the multicast joining (4) Multicast the requested channel

Network(multicasting and unicasting)

Access Node

Access Node

Access Node

VideoSource

(1)

(2)

(3)

(4)

Channelchangeserver

(2) Unicast the retained I-frame for FFC

Figure 2: A typical IPTV distribution architecture with CCS.

0 4:00 8:00 12:00 16:00 20:00 24:000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Local time

Rate

Figure 3: An illustration of periodic bursts of channel zappingevents (derived from [10]).

portion of online clients tend to conduct continuous channelzapping for a short period of time (e.g., several minutes),with the purpose to search for the channels of interest. Inaddition, we have also noticed the fact that the ends of theTV programs (or the starts of the commercial breaks) ondifferent channels are generally aligned at every half an hour.Therefore, from the perspective of the entire IPTV system,the rate of channel changing events is likely to periodicallyburst up to double or even triple of its average value, as isillustrated in Figure 3 (derived from [10]). Figure 3 presentsthe diurnal change of the aggregate channel changing eventsmeasured in a national-wide IPTV system with over twomillion clients in a representative working day. Particularly,it shows the timeline of these bursts, which are defined as thetotal number of channel changes normalized by the numberof online clients. We can observe very strong spikes with theperiod of every 30 minutes, which reflect the correlated useractivities due to the temporary alignment of TV programs.

As already indicated, when the I-frame accelerationmechanism is used, a separate IP unicasting stream is neededto deliver the retained I-frame of the requested TV channelto a client upon the arrival of the client’s channel change

request. Thus, each request will consume amount of systemresources for a short period of time until the completionof the retained I-frame transmission. This is fundamentallydifferent from the IP multicasting communication where theresource demands are directly determined by the number ofmulticasting channels. Furthermore, if we take the periodicburst feature of channel changing requests into account, theresource demands of the I-frame acceleration mechanismmay become much larger than what the IPTV system canafford. Consequently, the scalability of the original I-frameaccelerationmechanism can be severely degraded in practice.In particular, the scalability is mainly restricted by two fac-tors, namely, the CCS’s capacity (e.g., I/O capacity), as well asthe bandwidth capacity of the links in the IPTV transmissionnetwork (e.g., the links between the CCS and clients). Thedemands regarding the above two aspects will be linearlyincreased as the number of concurrently served channelchanging requests grows. In general, the CCS’s capacity canbe enhanced by employing more powerful servers, which isrelatively easy compared to upgrading transmission networks(the second factor). The reason is clear that the transmissionnetworks consist of a tremendous amount of links andintermediate nodes; most of the service providers cannotafford the costs of upgrading them (even in a medium scale).Therefore, it is highly desirable to propose efficient and easy-implementing schemes in order to optimize the scalability ofthe most widely used I-frame acceleration mechanism, par-ticularly during commercial breaks of peak-hour durations.

4. A Fairness-Based Admission Control(FAC) Scheme to Optimize the I-FrameAcceleration Mechanism

In this section, aiming to optimize the scalability of the I-frame accelerationmechanism,we propose a novel admissioncontrol scheme by taking the fairness into account. The prosand cons of using this FAC scheme are also discussed.

4.1. General Assumptions. We investigate the IPTV servicesystem as illustrated in Figure 2, where a CCS is installed as


the entity to perform the I-frame acceleration mechanismbased FCC methods. In addition, we assume the following.

(i) The service provider has reserved sufficient band-width in the delivery network for the concurrentmulticasting of all the provided TV channels. If thereare in total 𝐾 different TV channels provided andassume that all of them are constant bit-rate (CBR) ornear CBR encoded, then the bandwidth reservation𝐵multi on each link in the delivery path from theVideo Source to every Access Node is fixed and isequal to the summation of all the channels’ bandwidthdemands. Particularly, 𝐵multi = ∑

𝐾

𝑖=1𝑟multi,𝑖, where

𝑟multi,𝑖 is the bit-rate for channel 𝑖. Note that 𝐵multi isirrelevant to the number of online clients but only thenumber of provided TV channels.

(ii) Upon receiving a channel change request froman IPTV client, the CCS immediately transmits aretained I-frame of the requested TV channel 𝑖 to theclient by means of IP unicasting, with a CBR, whichis generally higher than the channel’smulticasting bit-rate.

(iii) In order to deploy the I-frame acceleration mecha-nism based FCC methods, the service provider alsoreserves extra bandwidth on each link in the deliverypath from the CCS to every Access Node to unicastthe retained I-frames. However, due to resource limi-tations, the I-frame acceleration mechanismmay runout of the reserved bandwidth on some bottlenecklinks. As a result, a portion of the unicasting flows car-rying the retained I-frames for FCC may be blocked.Consequently, QoE degradation will be introducedto the clients downstream of the bottleneck link(s),especially during commercial breaks in peak-hours.

(iv) At a random instant of time, an online IPTV client isin one of the two modes, namely, zapping mode andviewing mode. In zapping mode a client continuouslychanges channels in a short time, while the viewingmode denotes a client keeping to watch a channel fora sufficiently long period of time (e.g., over 5minutes).

(v) The number of channel change events generated byclients is correlatedwith the timeline of TVprograms;that is, they burst during every commercial breakwiththe interval of half an hour (cf. Figure 3).

4.2. A Fairness-Based Admission Control Scheme. In practice,an IPTV client may zap TV channels quite frequently duringcertain periods of time. We define this as the client’s heavyzapping behaviour, which introduces a large amount of extratraffic loads to the transmission networks. The bandwidthreservation for deploying the FCC methods is limited inthe transmission networks. Therefore, we believe it is notfair that the heavy zapping behaviour demands too muchbandwidth resource and accordingly incurs negative influ-ences on the clients who just occasionally zap channels.Taking this into consideration, we propose an optimizationsolution called fairness-based access control (FAC) scheme

to restrict the heavy zapping behaviours of some clientsand, consequently, to eliminate their negative impacts onthe underlying transmission networks. The principle of thisscheme can be depicted by the following three points.

Point 1. Monitor all the online IPTV clients by recording theirrecent channel changing histories.

Point 2. Based on Point 1, dynamically categorize a client intoeither the Heavy Zapper group or the Light Zapper group atinstants this client changes channels.

Point 3. Taking the fairness into account, actively disable theunicasting of the retained I-frame to a client who is currentlyin the Heavy Zapper group.

More specifically, two logical modules are newly intro-duced into the IPTV service architecture as presented inFigure 2. They are Monitoring and Statistics Module (MSM)and Access Control Module (ACM), respectively. Due tothe fact that both of them are logical modules, they can beinstalled close to or even be directly implemented on the CCSin practice.

MSM is adopted to conduct the following two tasks. First,for all the online clients, MSM monitors and calculates thefrequency of the channel changing requests arriving at theCCS in real-time. Second, for each single client, MSMmoni-tors its channel changing history by tracking the informationof the client’s most recent channel zapping events (excludingpossible channel viewing events). More specifically, MSMcontinuously records and updates the zapping dwell time (i.e.,the duration a client browses a channel in the zapping mode)for each of the online clients; for client 𝑖, the zapping dwelltime corresponding to the most recent 𝑁 channel zappingevents will be written into an array 𝑍

𝑖,𝑗(1 ≤ 𝑗 ≤ 𝑁). Besides,

MSM introduces a different weight 𝛼 for the zapping dwelltime connecting to the most recent channel zapping event(i.e., 𝑍

𝑖,1), which is usually considered to be more important

than the other elements in 𝑍

𝑖,𝑗to reflect the client’s most

recent behaviour and to predict the future trend. On thatbasis, the average zapping dwell time 𝑇

𝑖can be expressed by

(1). In particular, three more detailed situations are presentedin Figure 4. The first situation is demonstrated in Figure 4(a)where the client’s past𝑁 zapping events are continuous, and𝑇

𝑖can be evaluated straightforward by (1). Figure 4(b) consid-

ers another situation with channel viewing event(s) amongstthe𝑁 zapping events. Thus, the view events should be elimi-natedwhen𝑇

𝑖is evaluated. Figure 4(c) illustrates the third sit-

uation with the client just turned on the TV, and currently thetotal number of zapping events 𝑛 is smaller than𝑁. Thus, (2)is employed to replace (1) for the calculation of 𝑇

𝑖as follows:

𝑇

𝑖= 𝛼 ⋅ 𝑍

𝑖,1+ (1 − 𝛼)

∑

𝑁

𝑗=2𝑍

𝑖,𝑗

𝑁 − 1

,

(1)

𝑇

𝑖=

∑

𝑛

𝑗=1𝑍

𝑖,𝑗

𝑛

.

(2)

ACM is a logical entity to conduct the FAC scheme for allthe I-frame unicasting flows served by the CCS. It is clear thateach unicasting flow carries a retained I-frame of a requested


456

4733

S1With continuouszapping

34

Ti = 4.2 s

𝛼 = 0.2

t

N = 5

(a)

5

4

5684

70033

S2With viewingevent(s) inbetween

34

Ti = 4.4 s

𝛼 = 0.2

t

N = 5

(b)

On33

S3With the client just turned on

34 𝛼 = 0.2

t

N = 5

n = 3

Ti= (3 + 3 + 4)/n = 3.33 s

(c)

Figure 4: Three more detailed situations for applying the FAC scheme.

zapping dwell time for

Start to unicast theretained I-frame to

the client

Evaluate Ti the average

Client i’s request ofTV channel a arrives

Do not unicast theretained I-frame to

the client

No

Yes

Ti < Tthd

the online client i

Figure 5: Flow chart to illustrate the FAC scheme.

TV channel with the aim to accelerate the channel changingprocess for the client requesting that channel. However, eachunicasting flow consumes a considerable large amount ofnetwork bandwidth resources (although each unicasting flowmay not last for a long time).When there is a burst of channelchange requests arriving at the CCS, ACM is responsible formaking sure that the limited network bandwidth resourcescan be usedmore fairly and efficiently, so that acceptable QoEcan still be maintained for the clients. The implementation ofour proposed FAC scheme can be demonstrated by the flowchart in Figure 5. As can be seen, the key step is to comparethe evaluated 𝑇

𝑖and a threshold value 𝑇thd, which is used to

decide whether the current client is a heavy zapper or not.Similar to 𝑇

𝑖, the value of 𝑇thd is also dynamically

determined and updated by the MSM. There are manydifferent solutions to do this. One exemplary method may

be time interval based. More precisely, after one fixed periodof time, 𝑇thd should be recalculated according to the mostrecent updates of client requests arriving intensity. Anotherexemplary method could be based on the change of the clientrequests arriving intensity. In this case, if a currently updatedrequests intensity becomes significantly different than themean value of several most recently evaluated intensities,either absolutely or relatively, 𝑇thd should be updated. Theflow chart in Figure 6 demonstrates the process to determineand update 𝑇thd with algorithm 𝑋. Note that looking for adecent algorithm 𝑋 is outside of the scope of this paper andis left for further study. To summarise, Figure 7 demonstratesthe functionalities and the relations between the two logicalmodels (i.e., MSM and ACM).

4.3. Advantages and Disadvantages of the FAC Scheme. Theadvantage of using our FAC scheme is obvious. With theFAC scheme, the network bandwidth demands by the I-frame acceleration mechanism can be decreased; the CBP onthe CCS due to bandwidth limitation can also be reduced.Accordingly, the scalability of the original I-frame accelera-tion mechanism can be effectively optimized.

On the other hand, the above advantages are not achievedwithout cost. Our FAC scheme sacrifices the profits ofcertain clients who are currently conducting heavy zapping(i.e., in the Heavy Zapper group), by means of activelyand intelligently blocking the retained I-frame transmissionsfor FCC. However, we believe that this punishment forthe heavy zapping behaviour preserves the profits for theclients in the light zapper group. As a result, the I-frameaccelerationmechanism can treat different clients more fairlywith adopting this proposed FAC scheme.

5. Performance Evaluation byMeans of Simulation

In this section, the performance of our proposed FAC schemeis evaluated by means of simulation. In particular, we presenta case study concerning a typical commercial break in peak-hours.

5.1. Simulation Introduction. We focus on a single bottlenecklink LN between the CCS and a certain Access Node and


Algorithm X needs to

Update the value of thethreshold Tthd by algorithm X

Client i’s request ofTV channel a arrives

No

Yes

Update channelrequests arriving

intensity

Keep the value of the

unchangedthreshold Tthd

update the value of Tthd

Figure 6: Flow chart to show the determination of 𝑇thrd.

Client requests arrivingintensity monitor

Single client’s channelzapping history recorder

Single client’s zappingfrequency calculator

calc

ulat

or

MSM

Zapping frequency andthreshold comparison

FCC I-frame unicastingcontroller

ACM

Ti, Tthd

Thre

shol

dT

thd

Figure 7: The functionalities and the relations between the MSM and the ACMmodules.

assume that there is no other bottleneck links in the channeldelivery path. On the link LN, two different performancemetrics, that is, the real-time bandwidth demand and theCBP for FCC introduced by the I-frame acceleration mecha-nism; are evaluated. Here the second metrics are defined asthe probability of blocking the retained I-frame unicastingstreams due to resource limitation. To this end, we havedeveloped a trace-driven simulation model based on the n-dimensional state vector below:

(UserBW 1,UserBW 2, . . . ,UserBW 𝑛) , (3)

where 𝑛 is the total number of clients downstream of thelink LN. The vector is used for recording the current statusof the clients; that is, the 𝑖th element UserBW 𝑖 in the vectorrepresents the current bandwidth requirement introduced by

FCC for client 𝑖. Note that UserBW i should be 0 exceptin the duration when the CCS is expected to deliver aretained I-frame to client 𝑖. Thus, the summation of all theelements is the aggregate bandwidth demandonLN for all thedownstream clients. The simulation consists of the followingfour steps.

Step 1. Generate a traffic trace for each of the clients down-stream of the link LN, which should include channel requestarriving events, as well as FCC completion events (i.e., thefinish of transmitting a retained I-frame). Besides, the lengthof each single trace should be larger than or at least equal to𝑇cb, the time duration will be simulated.

Step 2. Derive an aggregate trace for all the clients bycombining all the single traces generated in Step 1.


Step 3. Start to simulate client channel changing events aswell as FCC completion events according to the aggregatetrace. During the simulation, the bandwidth requirementon the link LN for I-frame accelerations is monitored andrecorded in real-time.

Step 4. At the end of the simulation, CBPs for FCC in boththe entire simulation duration 𝑇cb and in a certain portionof it are evaluated based on the recorded call blocking eventsand the total number of channel change requests during thesimulation.

Weneed to study two different types of situations, namely,the situations with and without using our proposed FACscheme, with the aim to demonstrate that to what extent ourFAC scheme can improve the scalability of the original I-frame acceleration mechanism.

During the simulation, the transmission of a retained I-frame (for FCC) may be blocked either due to bandwidthresource constraints or caused by using the proposed FACscheme. In that case, the channel changing acceleration fora specific client request is failed, and the client has to wait forextra time to get the first I-frame (in the multicasting streamof the requested channel) to be displayed on the screen. Wecall this extra time directly introduced by the FAC scheme asFCC failure delay (FFD). The Average FFD is monitored asthe third performance metric, which is defined as the sum ofFFDs normalized by the total number of channel requests inthe simulated time slot.

5.2. Case Study: Application of the FAC Scheme in a Typi-cal Commercial Break in Peak-Hours. Based on the abovesimulation model, we conduct performance evaluations forthe FAC scheme when it is applied to a typical commercialbreak (with length 𝑇cb) in peak-hours. When a large numberof channel requests burst, the heavy traffic load introducedby using the I-frame acceleration mechanism based FCCmethods can strongly impact on IPTV systems. Below aresome additional assumptions for this case study.

(i) The system provides both SDTV and HDTV chan-nels, both of which are CBR encoded with the multi-casting transmission bit-rates 𝑅Multi,SD and 𝑅Multi,HD,respectively.

(ii) When a client conducts a channel change for anSDTV (or HDTV) channel, the CCS promptly uni-casts a retained I-frame of the requested channelto the client with CBR 𝑅Uni,SD (or 𝑅Uni,HD), whichis generally larger than the channel’s multicastingbit-rate 𝑅Multi,SD and satisfies (4) (or (5) for HDTVchannel). Consider

𝑅Uni,SD = 𝛼SD ⋅ 𝑅Multi,SD (𝛼SD > 1) , (4)

𝑅Uni,HD = 𝛼HD ⋅ 𝑅Multi,HD (𝛼HD > 1) . (5)

(iii) The bandwidth reservation on the bottleneck link LNfor unicasting the retained I-frames is fixed as BW (inpractical IPTV systems, BW should be determined by

the service providers with considering other factors,such as the average offered traffic).

(iv) The number of IPTV clients downstream of the linkLN is fixed as 𝑁client during the considered commer-cial break, whichmeans that there is no client enteringinto or leaving from the system. The clients canchoose both SDTVandHDTVchannels (i.e., with theprobability 𝛽 to select SDTV channels). In addition,these clients behave independently.

(v) Each of the clients begins to zap TV channels oncethe commercial break starts. A client continues to zapchannels for 𝐿 times. 𝐿 conforms to a truncated Pois-son distributionwith the mean value 𝐿, the maximumvalue 𝐿MAX, and theminimum value 𝐿MIN. After eachround of channel zapping, a client enters into viewmodel (i.e., watches a channel for a relatively longtime). Thus, the probability mass function (PMF) of𝐿 can be expressed by (6), where 𝐿MIN ≤ 𝑘 ≤ 𝐿MAXshould be satisfied. Consider

𝑓 (𝑘; 𝐿) = Pr (𝐿 = 𝑘) =

(𝐿

𝑘

⋅ 𝑒

−𝐿) /𝑘!

∑

𝐿MAX𝑗=𝐿MIN

((𝐿

𝑗

⋅ 𝑒

−𝐿) /𝑗!)

. (6)

(vi) For each single client, the zapping dwell time 𝑇zdt(denoting the duration a client browses a certainchannel in the zapping mode) conforms to a trun-cated negative Exponential distribution with the aver-age value 𝑇zdt, the maximum value 𝑇zdt,MAX, and theminimumvalue𝑇zdt,MIN.Thus, the probability densityfunction (PDF) of 𝑇zdt can be expressed by (7), where𝑇zdt,MIN ≤ 𝑥 ≤ 𝑇zdt,MAX should be satisfied

𝑓(𝑥;

1

𝑇zdt) =

𝑇zdt ⋅ 𝑒−𝑇zdt ⋅𝑥

∫

𝑇zdt,MAX

𝑇zdt,MIN(𝑇zdt ⋅ 𝑒

−𝑇zdt ⋅𝑥)

. (7)

(vii) Assume that the size of the retained I-frames con-forms to a truncated Normal distribution. Morespecifically, for an SDTV channel the mean value,the maximum value, and the minimum value ofthe I-frame size 𝑆SD are 𝑆SD, 𝑆SD,MAX, and 𝑆SD,MIN,respectively. The standard deviation of 𝑆SD is 𝜎sd.On the other hand, for an HDTV channel the meanvalue, the maximum value, and the minimum valueof the I-frame size 𝑆HD is 𝑆HD, 𝑆HD,MAX and 𝑆HD,MIN,respectively. The standard deviation of 𝑆HD is 𝜎hd.Thus, the PDFs of 𝑆SD and 𝑆HD can be expressedby (8) and (9), respectively. Besides, the inequalities𝑆SD,MIN ≤ 𝑥 ≤ 𝑆SD,MAX, and 𝑆HD,MIN ≤ 𝑦 ≤ 𝑆HD,MAXshould be respected. Consider

𝑓 (𝑥) =

(1/√2𝜋𝜎

2

sd) ⋅ 𝑒−((𝑥−𝑆SD)

2

/2𝜎2

sd)

∫

𝑆SD,MAX

𝑆SD,MIN[(1/√2𝜋𝜎

2

sd) ⋅ 𝑒−((𝑥−SSD)

2

/2𝜎2

sd)]

, (8)

𝑓 (𝑦) =

(1/√2𝜋𝜎

2

sd) ⋅ 𝑒−((𝑦−𝑆HD)

2

/(2𝜎2

hd))

∫

𝑆HD,MAX

𝑆HD,MIN[(1/√2𝜋𝜎

2

hd) ⋅ 𝑒−((𝑦−𝑆HD)

2

/2𝜎2

hd)]

. (9)


Table 2: Parameter values for the case study.

Notation Description Values𝑇cb Length of the considered commercial break 5 MinutesBW Bandwidth reserved for unicasting the retained I-frames for FCC on the

bottleneck link LN 500Mbps

𝑁client The number of IPTV clients downstream of the link LN 4000𝛽

The probability a client chooses an SDTV channel when conducting thechannel changing 0.7

𝑅Multi,SD CBR of multicasting an SDTV channel 4Mbps𝑅Multi,HD CBR of multicasting an HDTV channel 8Mbps𝛼SD I-frame acceleration ratio for an SDTV channel 1.2𝛼HD I-frame acceleration ratio for an HDTV channel 1.4𝑅Uni,SD CBR of unicasting the retained I-frame for SDTV channels 4.8Mbps (=𝛼SD ⋅ 𝑅Multi,SD)𝑅Uni,HD CBR of unicasting the retained I-frame for HDTV channels 11.2Mbps (=𝛼HD ⋅ 𝑅Multi,HD)

𝐿

Number of zapping times in the commercial break; conforming to a truncatedPoisson distribution (𝐿 is the mean, 𝐿MAX is the upper bound, and 𝐿MIN isthe lower bound)

𝐿 = 15; 𝐿MAX = 100; 𝐿MIN = 5

𝑇zdt

Zapping dwell time (i.e., the duration a client browses a certain channel in thezapping mode) conforms to a truncated negative Exponential distribution(𝑇zdt is the mean, 𝑇zdt,MAX is the upper bound, and 𝑇zdt,MIN is the lower bound)

𝑇zdt = 10 s; 𝑇zdt,MAX = 20 s;𝑇zdt,MIN = 1.5 s

𝑆SD

Size of an I-frame in an SDTV channel, which conforms to truncated Normaldistributions (𝑆SD is the mean, 𝑆SD,MAX is the upper bound, 𝑆SD,MIN is thelower bound, and 𝜎sd is the standard deviation)

𝑆SD = 800Kb; 𝑆SD,MAX = 960Kb;𝑆SD,MIN = 640Kb; 𝜎sd = 100Kb

𝑆HD

Size of an I-frame in an HDTV channel, which conforms to truncatedNormal distributions (𝑆HD is the mean, 𝑆HD,MAX is the upper bound, 𝑆HD,MINis the lower bound, and 𝜎hd is the standard deviation)

𝑆HD = 1.6Mb; 𝑆HD,MAX = 1.92Mb;𝑆HD,MIN = 1.28Mb; 𝜎hd = 0.2Mb

𝑇Iframe,SD Time needed to transmit a retained I-frame of an SDTV channel 𝑇Iframe,SD =

𝑆SD

𝑅Uni,SD

𝑇Iframe,HD Time needed to transmit a retained I-frame of an HDTV channel 𝑇Iframe,HD =

𝑆HD

𝑅Uni,HD

𝐷SD

FCC failure delay (FFD) for an SDTV channel; subjects to a truncatednegative Exponential distribution (𝐷SD is the mean,𝐷SD,MAX is the upperbound, and𝐷SD,MIN is the lower bound)

𝐷SD = 0.6 s;𝐷SD,MIN = 0.2 s;𝐷SD,MAX = 1 s

𝐷HD

FCC failure delay (FFD) for an HDTV channel; subjects to a truncatednegative Exponential distribution (𝐷HD is the mean,𝐷HD,MAX is the upperbound, and𝐷HD,MIN is the lower bound)

𝐷HD = 0.8 s;𝐷HD,MIN = 0.3 s;𝐷HD,MAX = 1.3 s

(viii) The time needed to transmit the retained I-frame isdenoted as 𝑇I-frame,SD and 𝑇I-frame,HD for the SDTVand the HDTV channels, respectively. 𝑇I-frame,SD and𝑇I-frame,HD can be directly determined by the size ofthe I-frame and the unicasting bit-rate. Consequently,(10) should be fulfilled as follows:

𝑇I-frame,SD =

𝑆SD𝑅Uni,SD

,

𝑇I-frame,HD =

𝑆HD𝑅Uni,HD

.

(10)

(ix) As discussed in the previous subsection, FFD isdefined as the extra channel changing latency broughtby the FAC scheme. A set of factors including thoselisted in Table 1 can contribute to FFD.Therefore, it isreasonable to assume that FFD subjects to a truncatednegative Exponential distribution. For the SDTV andHDTV channels, we denote FFD as 𝐷SD and 𝐷HD,respectively.

Table 2 lists the parameter values we used for this casestudy. In the simulation experiments, we have applied ourFAC scheme with setting the parameters 𝑁 = 3, 𝛼 =

0.5 (cf. Section 4.2). Besides, three different values for thethreshold 𝑇thd are tested.The simulation results are plotted inFigures 8–11.More specifically, Figures 8, 9, and 10 present thebandwidth demand variations during the simulated commer-cial break for the three different values of 𝑇thd, respectively.In each of these figures, the upper curve represents thesituation with only using the original I-frame accelerationmechanism.As can be seen, the aggregate bandwidth demandincreases dynamically once the simulation starts. As timegoes, it reaches a relatively stable status; namely, the band-width demand vibrates around a fixed value. After that, thebandwidth demand starts to decrease slowly and graduallygets close to zero at the end of the commercial break. Theother curve located underneath is for the situation of usingour FAC scheme with a specific value of 𝑇thd. The lowercurve illustrates similar features as the upper one. However,it demands much less bandwidth (e.g., up to 50% percent lessin Figure 10) compared to the situation when FAC scheme is


800

700

600

500

400

300

200

100

00 30 60 90 120 150 180 210 240 270 300

Without FACWith FAC

Time (s)

Band

wid

th d

eman

d (M

pbs)

Bandwidth demands w/ and w/o FAC scheme (Tthd = 4 s)

Figure 8: Bandwidth demand on link LN for transmitting theretained I-frames w/ and w/o using the FAC scheme (𝑇thd =

4 seconds).

800

700

600

500

400

300

200

100

0

Band

wid

th d

eman

d (M

pbs)

0 30 60 90 120 150 180 210 240 270 300

Time (s)

Without FACWith FAC



5 seconds).

not applied. In addition, when comparing all the lower curvesin the three figures, we observed that our FAC scheme with alarger value of𝑇thd can introduce amore significant reductionof bandwidth demands. In Figure 11, we present the resultsof the CBPs for FCC evaluated for the intervals of every 30seconds (in this case, we get 10 (=5min/30 s) sampling pointson each curve) in the simulated commercial break. There arein total four curves presented, including the uppermost onefor the situation without using the FAC scheme and the threelower ones using the FAC scheme but corresponding to thethree different values of 𝑇thd. We can observe that the largerthe 𝑇thd is, the lower the corresponding curve locates, that is,the smaller the CBPs for FCC are.

The four figures above illustrate the benefits of usingour FAC scheme. Those gains are achieved by riding thecost of increasing channel changing delay for the heavyzappers. It is agreed that a thorough investigation of the costis indispensable for a complete study. We therefore evaluatethe Average FFD in a scenario where limited bandwidth

800

700

600

500

400

300

200

100

00 30 60 90 120 150 180 210 240 270 300

Time (s)

Band

wid

th d

eman

d (M

pbs)

Without FACWith FAC



6 seconds).

CBP for FCC w/ and w/o FAC scheme70

60

50

40

30

20

10

0

1 2 3 4 5 6 7 8 9 10

CBP

for F

CC (%

)

ith interval (in the commercial break)

Without FACWith FAC (4 s)

With FAC (5 s)With FAC (6 s)

Figure 11: FCC CBP evaluated on link LN w/ and w/o applying theFAC scheme for different values of 𝑇thd.

reservation is assumed on the bottleneck link LN (i.e., BW =

500Mbps). This indicates that an FCC failure event can beeither attributed to the bandwidth limits or incurred by usingthe FAC scheme.The simulation results for the three differentvalues of 𝑇thd are plotted on Figure 12. As can be seen, theaverage FFD linearly increases as 𝑇thd grows. The generaltrend of each curve is to slowly decrease as the time goes inthe simulation duration. The maximum value is around 0.27second in the curve 𝑇thd = 6 𝑠. Since it appears just at thebeginning of the commercial break when increasingly moreclients start to zap channels, we believe it is acceptable as anaverage FFD value. And we also note that for heavy zappersthe situation would be worse, while for clients not addicted tochannel zapping, the average delay would be much shorter.

To summarise, the simulation results imply that our FACscheme can effectively optimize the scalability of the originalI-frame accelerationmechanism based FCCmethods by dra-matically decreasing the bandwidth demands. In particular,if the bandwidth reservation for transmitting the retained I-frames is limited in the delivery network, the FAC scheme


ith interval (in the commercial break)

With FAC (4 s)With FAC (5 s)

With FAC (6 s)

0.30

0.25

0.20

0.15

0.10

0.05

0.00

1 2 3 4 5 6 7 8 9 10

Aver

age F

FD (s

)

Average FFC failure delay in BW limited scenario

Figure 12: Average FFC failure delay (FFD) for different values of𝑇thd in bandwidth limited scenarios (BW = 500Mbps).

is capable of significantly reducing the CBPs for FCC, withonly slightly increasing the average channel changing delay(i.e., average FFD). It is also observed that, when the reservedbandwidth is fixed, our proposed FAC scheme performsbetter in terms of reducing bandwidth requirements andCBPfor larger values of 𝑇thd. It is however not true for the averageFFD, which just gets worse as 𝑇thd increases. In addition,we emphasis that, as an important scalability optimizationmethod for the original I-frame acceleration mechanism, theFAC scheme is easy to be implemented by IPTV serviceproviders in reality; the performance of the FAC schemeis closely related to both the bandwidth reservation for theI-frame acceleration mechanism, as well as the value of 𝑇thd.Therefore, IPTV service providers should carefully choosethe values for these parameters based on their practical needs.

6. Summary and Outlook

In this paper, we focused on optimizing the scalability ofthe I-frame acceleration mechanism, which is currently mostwidely used in IPTV systems to decrease channel changedelay caused by I-frame acquisition. Existing literature indi-cates that channel changing events in an IPTV system aretemporally correlated and tend to be periodically bursting(cf. Figure 3). Due to this phenomenon, applying the I-frame accelerationmechanismmay introduce a large amountof extra traffic loads which can run out of the availableresources (in particular, the limited network bandwidth) inIPTV systems. Accordingly, the scalability of the I-frameacceleration mechanism can be largely degraded, especiallyduring commercial breaks in peak-hours. Aiming to solvethis problem (i.e., optimize the scalability), we proposed aneasy-implementing fairness-based admission control (FAC)scheme, as a scalability optimization method for the originalI-frame acceleration mechanism. The performance of theFAC scheme is evaluated by comprehensive simulation exper-iments. The simulation results show that our proposed FACscheme has the ability to significantly decrease the bandwidth

demand as well as the CBP for FCC. Hence it can optimizethe scalability of the I-frame acceleration mechanism, withonly slightly increasing the average channel changing delayby temporarily disabling FCC for the clients who are addictedto frequent channel zapping.

Our planned work in the future will be the derivationof an optimized algorithm to dynamically determine thethreshold value 𝑇thd, with the purpose to further improve theperformance of the FAC scheme. On the other hand, morerealistic user behaviour models in terms of channel changingevents during commercial breaks are desired and should beproposed for the performance evaluation for the FAC scheme,when more practical measurement data become available forus.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

References

[1] H. Uzunalioglu, “Channel change delay in IPTV systems,” inProceedings of the 6th IEEE Consumer Communications andNetworking Conference (CCNC ’09), Las Vegas, Nev, USA,January 2009.

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[3] Lucent Technologies, “Optimization of IPTV multicast traffictransport over next generation metro networks,” in Proceedingsof the 12th International Telecommunications Network Strategyand Planning Symposium, November 2006.

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