Performance Analysis of a Finite Duration Multichannel Delivery
Method in IPTV
Weiqiang Sun
, Member, IEEE
, Kan Lin, and Yang Guan
Abstract—Reducing the channel change time is one of the major concerns of IPTV network deployment. This paper proposes mul-tiple channels being delivered to household set top boxes, with a fi-nite duration, to reduce channel change time. Unlike existing pro-prietary solutions that require additional equipment, or complex interactions between set top boxes and provisioning devices, the proposed method is easy to implement. We develop mathematical models to evaluate the bandwidth demand and channel change time of this method. We find that in a typical setup, the channel change time is reduced to 20%, yet the peak bandwidth increase on carrier’s uplink is less than 50%. We compare the investigated method with existing ones, and argue that it is a promising alter-native in terms of required bandwidth, channel change time and implementation complexity.
Index Terms—Bandwidth demand, broadband access, channel change time, channel zapping, IPTV.
I. INTRODUCTION
I
PTV SERVICE is generally provisioned in shared IP net-works, together with other services such as Internet surfing and Voice over IP. To increase bandwidth efficiency, IPTV uses a selective delivery approach, where only the requested channels are streamed to Set Top Boxes (STBs). This increases channel change time (CCT) significantly, which is now becoming an im-portant concern for IPTV deployment. CCT in IPTV can be decomposed into IGMP signaling delay and video streaming delay. IGMP signaling delay is the time needed for an STB to leave a multicast group by sending an IGMP leave message, and to join a multicast group by sending an IGMP join message. This delay is typically within tens of milliseconds. It is also depen-dent on network state and can be significantly larger in case the network device processing the IGMP requests is heavy-loaded. Video streaming delay is the time needed for an STB to de-mul-tiplex, decoding, decrypting and display the video stream. This delay is typically less than one second for MPEG-2 and can be as long as 2 seconds for H.264/MPEG-4 AVC and is thus the predominant part of CCT. The readers are referred to [1] for more discussions on channel change time composition.Reducing the CCT generally requires complex interaction be-tween STBs, network devices and video servers. Given the CCT decomposition mentioned above, the reduction of CCT can be realized in two aspects. As dynamics in the network such as
Manuscript received November 18, 2007; revised February 22, 2008. First published May 7, 2008; last published August 20, 2008 (projected). This work is supported by the Natural Science Foundation of China under Grant 60602010. The authors are with the State Key Lab on Advanced Optical Communication Systems and Networks, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China (e-mail: sunwq@sjtu.edu.cn).
Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TBC.2008.2000281
IGMP join/leave process and multicast tree creation/modifica-tion will increase CCT, it is desirable to broadcast high pop-ularity channels as close to viewers as possible. Viewers will only experience partial delay when they change channels among those popular ones thus reducing the average CCT. This ap-proach scales well with the number of viewers and network size since most of the network state change will happen locally. The drawback of this approach is that when the number of offered channels is large and the viewers have diverse watching prefer-ence, a considerable large amount of bandwidth has to be stati-cally allocated for these channels.
To reduce the video streaming delay, it is important to re-duce the interval between Intra-coded frames (I-frames) in a stream. To realize this, a high bandwidth stream with more fre-quent I-frames can either be multicasted in network, or delivered point-to-point to viewers upon channel change. The advantage of this approach is that it can lead to significant CCT reduc-tion. However, as I-frames have lower compression ratio, the viewer will experience transient bandwidth increase on his/her access line, hence this approach is not applicable to networks with limited access bandwidth. At the same time, additional server is needed to deliver point-to-point streams to viewers upon channel change and it may have scalability issues when the served population is large.
With the provisioned bandwidth in access networks contin-uously increasing, it will be viable to deliver a few number of channels to household STBs. By delivering the most intended channels upon channel change, the majority of CCT can be avoided. This approach is straightforward and has been dis-cussed in [2]. The authors focused on the mechanisms to support multiple (adjacent) channels delivery. However, no performance evaluation was given hence its applicability is still unclear. At the same time, as channel changes are usually rare, delivering multiple channels all the time to STBs is not bandwidth effi-cient.
In this paper, we propose to use finite duration multi-channel delivery to increase bandwidth efficiency. We develop mathe-matical models to evaluate the network demand and channel change time when a large number of viewers change channels during commercial breaks. We show that duration of 20 seconds is enough to reduce channel change time by 80%. We also dis-cuss the practical concerns of this method and argue that it is a promising alternative to existing solutions when taking into ac-count the CCT performance, scalability and deployment cost.
The rest of the paper is organized as follows. We first give a brief overview on existing works in reducing CCT in Section II. In Section III we introduce the network model and assumptions used in our analysis. Then we develop a model in Sections IV and V to describe single viewer behavior, through which we further develop the bandwidth demand and channel
change time models. In Section VI we present numerical results obtained through analysis and verify these results through sim-ulation. We present a brief discussion on randomized channel change and some practical concerns of the proposed method in Section VII. Finally we compare the proposed method with existing approaches and conclude this paper.
II. RELATEDWORKS
Due to the practical nature of IPTV, the efforts in reducing CCT have been seen more in industry than in academia. Mi-crosoft adopted a solution in which upon a channel change, a unicast stream with higher rate is used to feed the STB play-out buffer, so that the streaming delay can be reduced. To realize this, a separate video server is needed to serve viewers’ channel change demands. Also, special care must be taken so that the viewer will not experience video drift when reverting to the desired multicast stream. It can be expected that the more fre-quently viewers change channels, the more bandwidth is needed in carrier’s core network. In [3], the authors developed a model to analyse the bandwidth demand during commercial breaks and found in an example that peak bandwidth is twice the steady state demand.
In its Visual Quality Experience (VQE) technology, Cisco uti-lizes a network and standard based solution to enhance IPTV viewing experience [4]. By running Real Time Transport Pro-tocol (RTP) and Real Time Transport Control ProPro-tocol (RTCP) between STBs and edge routers, Cisco alleges to realize channel change within one second as well as error detection and re-pairing in video transmission. The performance of this approach under different situation has not yet been reported due to the lack of more detailed information. In [5], the authors from Lu-cent proposed to classify the channels according to popularity and deliver all the popular channels to Access Nodes so that most channel changes will be served locally by the access node, reducing part of the channel change time at the same time main-tain a cermain-tain level of bandwidth efficiency.
In [6], the authors proposed algorithms to dimension the number of channels that need to be delivered statically to Last Hop Router (LHR), as well as the number of I frames in sep-arate fast channel changing streams, such that certain channel change time requirement can be meet with minimal network bandwidth consumption. In [7], the authors proposed to sep-arate P and I frame streams to increase bandwidth efficiency. The authors further argue that by increasing the frequency of synchronization frames, the channel change time can also be reduced.
In general, the methods mentioned above are tradeoffs be-tween the achieved channel change time, bandwidth consump-tion and implementaconsump-tion cost. We will further present a tabular comparison between various types of methods, together with the proposed one, in Section VII.
III. MODELS ANDASSUMPTIONS
A. Network Model
The design of the access networks supporting IPTV services varies between service providers. It can be an FTTX solution
Fig. 1. Network model.
that has the potential of offering a dedicated wavelength to each household. Or it can be based on widespread and cheap Ethernet solutions. Regardless of the specific technologies, the generic high level network architecture can be depicted in Fig. 1. In the case of an FTTP architecture, in the place of Access Nodes are Optical Network Terminals (ONT), which does not have multi-cast replication capability. In the case of Ethernet based access networks, the Access Nodes are layer-two switches, which may or may not have multicast replication capability. The Aggrega-tion Node is generally the network device where subscriber-spe-cific control and management operations are enforced.
The multicast replication capability of a network node can re-duce IPTV traffic volume on its uplink. For an access node that has multicast replication capability, only one stream is needed on its uplink to serve multiple connected subscribers who are watching the same channel. However for an access node that has no multicast replication capability, the uplink must pro-vide streams for respective subscribers, even though they are watching the same channel. In this paper, we investigate the bandwidth demand on uplinks of multicast replication capable network nodes. These can be the links that connect Access Node to Aggregation Nodes, which may serve up to 100 subscribers, or the links that connect the Aggregation Nodes to the Metro Area Network (MAN), which usually serve more than 1000 sub-scribers. Given the number of IPTV viewers under a single ac-cess or aggregation node, the required steady state bandwidth on respective uplinks can be deduced by taking into account the number of provisioned TV channels and the statistical be-havior of viewers. However, as the channel changing bebe-havior of viewers will lead to additional bandwidth consumption, it is equally important to dimension the required bandwidth demand when a large number of viewers surf during commercial breaks.
B. Finite Duration Multi-Channel Delivery Method
In [2] the authors proposed to deliver adjacent channels to STBs, thus when channel changes occur, the streams of the re-quested channels are readily available for decoding and display. Although this method is straightforward and its improvement to channel change time is undoubted, it may have poor effi-ciency as channel changes are usually rare. In [8], the authors showed that 95% of the channel change happens during com-mercial breaks. In most cases, a viewer changes a channel upon
Fig. 2. Finite duration multi-channel delivery method. This is an example of three channel delivery. Upon each channel change, the requested channel and the two successive channels (so called extra channels later) are delivered concurrently to STB. Delivery of extra channels stops if no more changes occur after a certain duration (marked by concurrent delivery duration).
commercial break andsurfsuntil he/she find a channel of in-terest. The channel change behavior can thus be identified as a series ofsurfing process, each of which is a number of consecu-tive channel changes. Based upon this observation, we propose to deliver multiple channels to viewers’ STBs when he/she is in surfing process. As the surfing processes are sparsely distributed on the time line, this method can greatly increase the bandwidth efficiency on both the carrier’s uplink and the subscriber lines.
An example of the finite duration multi-channel delivery method is shown in Fig. 2. Assume an STB in steady state at the beginning. Right after the -th channel change, the STB enters thesurfing processand three consecutive channels are delivered concurrently. As there is no video data for the -th channel in the decoding buffer before this change, the viewer has to wait for the buffering time marked by the dark gray box. This is also the maximum time that a viewer will experience during channel change. At -th and -th channel changes, the viewer experiences no delay as the video data for the respective channels has been stored locally. Some time after the -th channel change (as marked by concurrent delivery duration in Fig. 2), the network stops delivering and -th channel and the STB again enters steady state. It is possible that the a viewer will experience partial, i.e. none zero, but less than aforementioned maximum delay. That happens when a viewer changes channel before the buffering time marked by the dark gray box is over. We will discuss different situations in more detail in Section V.
C. Channel List and the Surfing Model
The channel adjacency is defined by a channel list, which may not necessarily be the same on all viewers. A natural way to organize such a list is to arrange channels according to each viewer’s preference. More preferred channels are put in the front and given a lower channel id. It would also be interesting to or-ganize channels according to both program genres and viewer’s preference, so that he or she can more easily find interested pro-grams by simply surfing along the list. More complex channel recommendation methods that can achieve such a goal can be found in [9] and [10].
The problem of providing personalized channel list is com-plicated and can be affected by many factors. In this paper, we assume each viewer share the same channel list provided by the Service Provider. We also assume that the sequence of the channel is arranged by channel popularity given by Zipf’s law
[3], [6], [11]. According to this law, the probability for a viewer to watch the first channel in the channel list is twice the proba-bility to watch the second channel, three times of the probaproba-bility to watch the third one, and so on. Also as a routine in TV set program, we further assume that the channel list is “circular”. Any viewer surfs through the last channel in list will be redi-rected to the first one automatically. To facilitate the analysis, we regard the list of channels as an infinite list, in which the -th, -th, -th channels actually refer to the same channel. In this manner, circular channel change is auto-matically achieved.
The channel surfing behavior of each viewer can be abstracted as a biased coin-tossing process [3]. Upon commercial breaks, a viewer waits for some random time and tosses a coin to decide whether or not he/she will change the channel. If the coin comes up “head” then the viewer changes to the adjacent channel. This process repeats until the coin comes up “tail”. If we name each toss as a renewal, then the renewal process resembles Poisson process in that the interval between each toss follows negative exponential distribution, and the process terminates according to geometric distribution. Given the observation that channel changes occur upon commercial breaks, we can always model the arbitrary channel change behavior by a series of surfing pro-cesses. Each of these process starts upon commercial break. It is worth noting that this surfing process model applies to other channel changing situations as well. For example, a viewer may also change the channel at the end of a program, or merely for any other reasons. The significance of such a process under the finite duration delivery method is that the delivered channels are initialized every time at the beginning of each surfing process (i.e. at the first channel change). As the processes themselves are independent from each other, it is sufficient to study the band-width demand and channel change time in one single surfing process.
D. Notations
In this section, we define notations employed in the following part of the paper. We are given the network architecture as illus-trated in Fig. 1. We are also given the following parameters.
• : the number of viewers severed by a multicast capable network device.
• : the number of provisioned channels.
• : the concurrent delivery duration, i.e. the maximum time of extra channels being delivered after a channel change.
• : the delay a viewer will experience before any optimiza-tion method is in place. It includes the delay for an STB to request new data stream from the network and the time needed to process it (buffering, decoding and displaying). • : the parameter of Poisson process whose reciprocal is the
mean interval between two successive channel change. • : the probability that a viewer gets a “head” at each coin
tossing process.
We further introduce some notations for the analysis of band-width demand and channel change time:
• : the probability that a viewer watches the -th channel at .
• : the probability that the -th channel is delivered to a viewer’s STB at .
• : the probability that extra channels are delivered to a viewer’s STB at .
• : the expectation of number of channels delivered to a multicast capable network device at .
• : the expectation of bandwidth consumption on the carrier’s uplink at .
• : the channel change time.
• : the probability density function of the channel change time.
IV. BANDWIDTHANALYSIS
The transient surfing process of each viewer will increase the bandwidth demand on carrier’s uplink. Without loss of gener-ality, we evaluate the bandwidth demand when a large number of viewers enter transient surfing state simultaneously, triggered by a commercial break. It is straightforward that the obtained peak bandwidth demand provides an upper bound for arbitrary channel changing cases. In the following, we first describe the behavior of a single viewer in detail, then we develop models to estimate the total bandwidth demand on carrier’s uplink.
A. Channel Watching Probability
For , viewers are in steady state and the probability of watching a given channel is determined by Zipf’s law. For , we denote by the probability that a viewer watches the -th channel at , then
watching th channel when
channel changes in (1) The probability of watching the -th channel for is given by Zipf’s law:
watching the th channel for (2) Recall the renewal process described in the previous section. Let be the probability that a coin comes up “head” in one trial. There are two situations that a viewer will make channel changes in . The first one is that renewals arrives in , giving the viewer chances to toss the coin but the results must be all “head”. We mark this situation as “ renewals and
heads”. Since Poisson Process and geometric distribution are mutually independent, we get
renewals in and heads heads renewals in
renewals in (3)
where
renewals in (4)
The second situation allows more than renewals arriving in but the only first tosses come up “head” and the -th toss is “tail”. This situation is called “more than renewals but
heads”:
more than renewals in but heads only heads more than renewals in
more than renewals in (5) The necessary and sufficient condition of more than renewals in is that the -th renewal arrives within the time interval. We denote by the time between the -th and the -th renewal. They are independent and share a common negative exponential distribution. Take the sum
, , then follows Erlang-k distribution [12]. We thus get:
more than renewals in
(6)
Combining (3) (4) (5) (6), we obtain: channel changes in
(7)
Thus, we can get the explicit expression for (1):
(8) Note that the above equation is valid for an infinite channel list. Now let us take finite channel list into consideration. In Section III-C, we have expanded a finite channel list into an infinite periodical list. Thus if we denote by the ID of a channel in a finite list, where , and denote by the probability of watching the -th channel at , from (8) we have : (9)
However, a careful scrutiny of (8) indicates is close to zero when is very large. Thus we can approximate by
.
B. Probability of Delivering Extra Channels
Denoting the duration we deliver extra channels, it is easy to find that whether extra channels are delivered at depends on whether or not the viewer changes channel in the interval . The probability of at least one channel change in the interval is the summation of all possible cases in which at least one channel change occurs during and arbitrary number of channel changes have occurred during . The occrurrence of channel change during is independent from the number of channel changes in . Thus for all
, we have:
at least one channel change in
at least one channel change during channel changes in
at least one renewal in no less than one ”head”
renewals in
no less than one”head”s (10) The probability of events in an interval is given by theory of Poisson Process:
Poisson events in (11)
where is the length of the interval. Combining this with geo-metric distribution, (10) can be expressed and simplified as:
(12) For , extra channels will be delivered if channel changes occur during . Thus is given by:
more than renewal more than one “head”
(13) Thus, we have:
:
: (14)
C. Bandwidth Estimation
The probability of the -th channel being delivered to a viewer is the probability this channel being watched, plus the proba-bility that one of the channel being watched and extra channels are delivered. For simplicity we as-sume that a channel being watched is independent from whether
Fig. 3. Situations in which a viewer experience partial delayw. (a) The interval of the current channel change is less thanF and that of the previous change is greater than. (b) The intervals of the current and the previous channel changes satisfyt + t < F.
or not extra channels are delivered. Our simulation will show that this assumption does not incur significant difference to the results. Thus from Sections IV-A and IV-B, if we denote by the probability that channel is delivered to a viewer at time , we have:
(15)
where is the number of extra channels delivered. The expecta-tion of number of delivered channels to an access node at time
can be expressed as:
(16)
Assume that all channels are of standard definition whose data rate is 2.5Mbps, then the expectation of bandwidth demand is:
(17)
V. CHANNELCHANGETIMEANALYSIS
As we have mentioned in Section III, the experienced channel change time in the investigated channel delivery method is not constant. A viewer experiences full (or maximum) delay if the previous channel change occurred seconds before or earlier. A viewer may also experience zero or partial delay in situations illustrated in Figs. 3 and 4. It is worth noting that under any circumstances, the channel change time of the current change (marked by an up-going arrow in Figs. 3 and 4) only depends on the previous two channel changes. In the following, we analyse the channel change time of one surfing process. Because the delays of the first, second and later changes in a single surfing process follow different statistical models, we present analysis for each of these channel changes separately, based on which the overall channel change time performance is then be derived. We also present in this section an estimation of channel change time when typical viewer behavioral parameters are applied.
A. CCT Expectation for Each Channel Change in One Surfing Process
1) The First Change: A viewer will always experience full delay at the first channel change for each surfing process. Thus the expectation for this case is .
Fig. 4. Situations in which a viewer experience zero delay. (a) The interval of the current channel changetsatisfiesF t < while that of the previous change is greater than. (b) The interval of the current and the previous channel changes satisfyt < FandF t which implies an continuity of the object stream at the previous change.
2) The Second Change: At the second channel change for each surfing process, a viewer may experience full, partial and zero delay under different situations. As the zero delay cases have no contribution to the overall channel change time, we omit it here. Instead, we use a separate sub-section in Section V-C to analyse the probability by which a viewer experiences no delay. A viewer experiences full delay if the channel change occurs after the concurrent delivery duration has already expired since last change. Denote the channel change time by , and then the probability can be expressed as:
(18)
Or if we regard as a continuous random variable, we get its probability density function
(19) where is the dirac-delta function. Note that this probability applies to the third and later changes as well.
The situation that a viewer experiences partial delay at the second change is illustrated in Fig. (3a). Since the interval
and that , we immediately get the probability density function of this case:
(20) Combing (19) and (20) we get the overall expectation of the second channel change
(21)
3) The Third and Later Changes: The situations that a viewer experiences partial delay at the third and later changes are il-lustrated in Fig. (3a) and (b). For Fig. (3a), the second change occurs later than seconds from the first change (not shown), while the third change occurs less then seconds after the second change. The probability density function for this case is then
(22)
For Fig. (3b), as , and conforms to Erlang distribution as in Section IV, the probability density function for this case is
(23) Combining (22) and (23), together with (19), we get CCT ex-pectation for the third and later channel changes:
(24)
B. Overall Expectation of CCT
Recall the Poisson process like surfing behavior model in Section III under which the viewer will toss a biased coin to de-termine whether to conduct another channel change in a surfing. In each surfing process the viewer will start his first change with probability , and then go on his second switch with , third switch with and so on. Thus, if adding the product of these probabilities and their corresponding delay expectation for the case of 3-channel delivery:
(25) the sum actually gives out the expectation of the total delay of one surfing process.
Meanwhile, we can easily derive from the aforementioned surfing model that the average number of channel changes in one surfing, denoted by ‘S’, is:
(26) namely,
(27) Substitute this relation into (25) and then divide the result by S, we obtain the overall average delay of each channel change in one surfing process for 3-channel delivery:
(28) The case of 2-channel delivery is analogous and the only dif-ference is that is replaced by this time. The ultimate average delay of each channel change in one surfing turns out to be:
(29) Because we have grouped arbitrary channel changes into the unit of surfing, it follows that the overall expectation of an arbitrary change is equal to that of a single change in one surfing. Hence, (28) and (29) both indicate the overall channel
change time performance of our method, given the parameter of which is influenced by .
C. Probability of Experiencing No Delay
A viewer experiences no delay at the second channel change if and only if the change occurs within but seconds later the first change (Fig. 4(a)). The probability for this case is then
(30)
For the third and later changes in a surfing process, no delay will be experienced for both the case in Fig. 4(a) and (b). As we have already deduced the probability density function of both full and partial delay for this case, we can immediately get the probability of experiencing no delay as follows:
(31)
D. Estimation of the CCT and Probability of Experiencing No Delay
Focusing on (24), we notice that CCT expectation is very much dependent on the concurrent delivery duration . Take the derivative of this expression with respect to , we get
(32) The constant factors in front of will drop in the interval of [ 8,0] for the typical values of in [1,4] and , the recip-rocal of average switch interval, in [0.2,1]. indicates that will decrease as increases. Whereas will drop dramatically at first and soon approach 0 as increases. This indicates that a moderate will suffice to reduce the CCT expec-tation to an acceptable value. Thus, if we consider
as steady, then will be less than 0.01, small enough for to approach zero. Then the recommended should be
(33) Things are similar for the case of 2-channel delivery. So if the average interval of viewer’s channel change event is 4 seconds, namely , then the duration is recommended to be 20 seconds. This will be verified by our simulation in Section VI.
Substitute in (21) and (24) by , we get :
: :
(34)
For in interval [0.2,1] and in [1,4], the CCT expectation
reach at worst and at best, for the
second and third channel change respectively. In particular,
Fig. 5. Bandwidth demand vs. number of extra channels delivered (number of viewers:500; delivery duration: 20 seconds; mean channel change times:10;
= 0:25).
Fig. 6. Bandwidth demand vs. delivery duration (number of viewers:500; 2 extra channels delivered; mean channel change times:10; = 0:25).
when and takes on the typical value of and , the result settles at and .
The overall expectation is further dependent on , subject to the determination of parameter . If we choose as 10, implying that the average number of channel changes in one surfing is 10, then the numerical estimation finally comes to:
2-3- (35)
The probability of experiencing no delay for the second and later changes increases as or decreases. Again for and
, the probability is
(36)
VI. SIMULATION
We developed simple simulation programs to verify the above models. In this section, we will present the results obtained by both mathematical calculation and simulation under different parameters. In both mathematical model and simulation, we as-sume 200 channels are provisioned to viewers. All channels are standard definition channels with equal data rate at 2.5 Mbps. The channel change time in an IPTV deployment without any optimization, or , is assumed to be 2 seconds. Figs. 5–9 il-lustrate the bandwidth demand versus time in a single surfing process. Fig. 5 shows the bandwidth demand when different
Fig. 7. Bandwidth vs. viewers scale (mean channel change times:10; delivery duration: 20 seconds; 2 extra channels delivered; = 0:25).
number of channels are delivered. In general, the more extra channels are delivered, the more bandwidth is required. How-ever, it can be observed that when the number of viewer is 500, the bandwidth increase in peak is about 40% for three channel delivery and 30% for two channel delivery. The required band-width decreases as more and more viewers will tune into their respective favorable channels and stop surfing. In a typical setup where the delivery duration is 20 seconds, the mean number of channel change is 10 and average arriving rate is 0.25, the band-width increase will reduce to half the peak value at 85 seconds in both three channel and two channel delivery case. It is worth noting that although the obtained peak bandwidth demand is ob-tained by assuming a large number of viewers start surfing uni-formly, it sets an upper bound for any other case where surfing of respective viewers are evenly distributed on time line.
Fig. 6 shows the bandwidth demand for different channel de-livery duration. The bandwidth increases slightly with the in-crease of duration. From the bandwidth point of view, one ex-pects shorter delivery duration to reduce bandwidth overhead on carrier’s uplink, as well as on viewers’ subscriber lines. How-ever, as we will see in the channel change time analysis, increase delivery duration is helpful to reduce the expectation of channel change time. The selection of channel delivery duration is thus a tradeoff between achievable channel change time reduction and bandwidth consumption.
In Fig. 7, we show the total bandwidth demand for different viewer populations. The peak bandwidth is more than twice of the steady value when the viewer population is small. How-ever, the peak bandwidth increase to its steady value is about 40% when the viewer population is large. This is because when the viewer population is large, more channels are likely to be watched and hence delivered by default. In this case delivering extra channels will not incur additional bandwidth consumption on carrier’s uplink. In another word, the efficiency of the pro-posed method increases when the served viewer population in-creases. This is a desirable merit as it allows for very large scale deployment.
Figs. 8 and 9 shows the bandwidth demand versus time with different viewer channel changing behavior. Fig. 8 shows the bandwidth demand with different average number of channel changes in a single surfing process when three channels are delivered. More bandwidth is required if viewers tend to surf more channels in a surfing process. Fig. 9 depicts how average channel change interval effects bandwidth demand. If viewers
Fig. 8. Bandwidth demand vs. average number of channel changes (number of viewers:500; delivery duration: 20 seconds; 2 extra channels delivered; =
0:25).
Fig. 9. Bandwidth demand vs. average renewal interval (Number of viewers:500; delivery duration: 20 seconds; 2 extra channels delivered).
change channels more quickly, the peak bandwidth arrives early while it falls back to steady value more quickly. The variation of peak bandwidth under different average channel change time interval is not significant. In general, Figs. 8 and 9 shows that the average number of channel changes has larger impact on band-width demand than the average channel change interval.
Fig. 10 depicts CCT expectation of the 3rd and later changes for different concurrent delivery durations. The simulation re-sults well agree with our mathematical analysis. The CCT de-creases dramatically at first and approaches optimum when the duration is 20 seconds. This indicates that it makes little sense to deliver multiple channels all the time to viewers’ STB. On the other hand, it is worth noting that the selection of delivery dura-tion is highly dependent on the interval of each channel change. Under the extreme circumstance where a viewer change chan-nels rarely and with very long interval every time, the required delivery duration will be considerably longer to achieve channel change time reduction. However, in practice, for such case one may choose not to reduce channel change time at all since the overhead of a singe channel change is neglectable as it comes rarely, if compared with continuous channel change with short intervals (i.e. surfing). Fig. 11 shows the overall expectation of CCT with respect to the average number of channel changes ( ) in one surfing process. The up-triangled and down-triangled curves represent the average CCT by mathematical calculation and simulation respectively. We see from the figure that CCT is relatively large when is small due to greater influence of the beginning channel changes. However, average CCT decreases
Fig. 10. Channel change time for the 3rd and later changes vs. concurrent de-livery duration( = 0:25).
Fig. 11. Channel change time vs. Average number of channel changes(delivery duration: 20 seconds; = 0:25).
when increases as it benefits more from the latter small-delay channel changes. The simulation curve accords with the mathe-matical one indicated by (28) and (29) very well. When a viewer tends to change channels often, it is more likely that he/she is more sensitive to channel change time. The proposed delivery method exhibits an attractive feature for such viewers. Figs. 10 and 11 also distinguish the case of 2-channel delivery from that of 3-channel delivery. The results show that CCT improvement for 3-channel delivery is much more satisfying than that for 2-channel delivery.
VII. DISCUSSIONS ON THEPROPOSEDMETHOD
A. Random Channel Change
The analysis and simulation presented above are based on the assumption of sequential channel change, in which a viewer changes from one channel to the next according to the channel list. In reality, viewers may also change to other channels rather than the next one. Recall from Section III-C that each surfing process starts from an initial channel change upon commercial
Fig. 12. Bandwidth consumption under randomized channel change be-havior(number of viewers:500; delivery duration: 20 seconds; 2 extra channels delivered; Mean channel change times:10; = 0:25).
break or end of a program. This initial channel change is then followed by a number of adjacent channel changes. Even though users may change channels randomly, it is still reasonable to be-lieve that their behavior still falls into the “surfing” model. Once a viewer terminates a surfing process by randomly selecting a channel, a new surfing process is initiated. Thus by adjusting the number of channel changes, our model can be applied to an-alyse the random channel change case. We used simulation to study the performance of the proposed method under random-ized channel change behavior. In the simulation, we introduced to denote the probability that a viewer will change to the ad-jacent channel. Fig. 12 shows the bandwidth demand under ran-domized channel change behavior. It is quite surprising that less bandwidth is needed when users select channels randomly. This is because in the standard model where only adjacent channel changes occur, viewers are more likely to stop at a less popular channel, while in the randomized model they tend to be packed toward popular channels. Fig. 13 shows the CCT performance under randomized model. Average delay will be longer when decreases, i.e. the randomness increases. However, owing to the good performance gained from the adjacent changes in a ran-domized surfing processes, the ultimate improvement of CCT is still substantial.
B. Practical Concerns of Implementing the Proposed Method
It is straightforward that the proposed method is compliant with existing IPTV infrastructure. Provided sufficient memory and computation power for multiple channel buffering and de-coding, it can be offered as an add-on feature to deployed STBs by software upgrade. This allows gradual deployment in net-works where subscribers have varied access bandwidth. This feature can be implemented in a way such that one viewer can select from the remote controller whether and how he/she would like to use this feature, according to his/her viewing habit. The parameters of the method, such as the number of delivered chan-nels and the concurrent delivery duration, can be fined tuned in an adaptive manner to reducing bandwidth consumption on ac-cess lines without sacrificing the CCT performance. Advanced program recommendation method can also be applied to achieve
TABLE I
A COMPARISONBETWEENDIFFERENTMETHODS FORREDUCINGCCT
Fig. 13. Channel change time vs. average number of channel changes under randomized channel change behavior (delivery duration: 20 seconds; 2 extra channels delivered; = 0:25).
optimal performance. One thing to note is that since the de-coding of the adjacent channel starts from the previous I-frame, an implementation may need to take special care so that the tiny time shift can be avoided.
C. Comparison With Different Approaches
As has been mentioned above, reducing the channel change time is generally a tradeoff between the achievable perfor-mance, implementation complexity and bandwidth demand. In designing such solutions, one assumption is that the sub-scriber line is now or will be able to provide more than enough bandwidth to deliver a few number of channels simultaneously to the home. The adoption of Fiber To The Home (FTTH) technology will further increase the available bandwidth on subscriber lines. Given the potential large population that an IPTV solution will have to serve, the real challenge actually lies in scalability and cost. We try to summarize the pros and cons of reported mechanisms in Table I. Our baseline solution is an IPTV deployment before any optimization method is in place.
VIII. CONCLUSIONS
In this paper, we propose a finite duration multi-channel de-livery method to reduce channel change time for IPTV. The pro-posed method requires no additional hardware so it is easy to im-plement and deploy. We develop mathematical models to eval-uate the performance of this method when viewers surf at com-mercial breaks. Both analytical and simulation results show that in typical setups, a duration of 20 seconds is enough to reduce
the channel change time by 80%. Also we find that the peak bandwidth caused by transient channel surfing of viewers has only 50% increase compared with steady state. We compared the proposed method with other reported ones and argue that it is a promising alternative to existing proprietary solutions, when taking into account the achievable performance, implementation complexity and bandwidth demand.
ACKNOWLEDGMENT
The authors would like to thank Prof. Yaohui Jin, Prof. Wei Guo and Prof. Weisheng Hu for their valuable discussions. The authors would also like to thank the anonymous reviewers for their insightful comments. The authors have equal contributions to this paper.
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