[ieee 2007 international conference on networking and services - athens, greece...

Abstract—The Internet’s explosive growth was spurred by a

variety of new and emerging applications and the simultaneous exponential growth of the Internet user community. In particular, multimedia applications are becoming increasingly important in the Internet and this makes the ubiquitous deployment of multicast, native or otherwise, an imminent event. As such, it is important to understand multicast traffic. This paper examines UDP consecutive packet losses and finds that this burst loss characteristic conforms approximately to the head of a geometric distribution which facilitates the derivation of an “ideal” burst loss distribution, assuming an ideal multicast network with no stray long burst losses.

Index Terms—multicast, burst losses, cumulative distribution, geometric distribution, and multicast traffic

I. INTRODUCTION

HE realization of the convergence of data, voice, and video over a single IP infrastructure seems imminent, as

evidenced by Cisco CEO John Chambers’ recent (mid November, 2005) statement: “Video is emerging as a key element in the service provider 'quadruple-play' bundle encompassing consumer entertainment, communications and online services. In fact, video may be the most critical element in this bundle for ensuring consumer differentiation and loyalty, or stickiness if you will, to service providers. The opportunity for Cisco is to dramatically reduce the complexity of converging data, voice, and video over IP in both a fixed and mobile environment, which is at the core of our expertise.” [1]

The lack of pervasiveness of Internet video has been attributed to the non-ubiquitous deployment of multicast [2]. Despite the bandwidth saving ability of multicast, embracement of the technology has been extremely slow due to protocol complexity, undefined billing models, an inclination to maintain the existing stability of the current unicast client connections, as well as unknown traffic and routing implications [2]. However, “in the last decade multicast has evolved from an experimental sub-scalable technology into a mature network service that providers are now deploying.” [3] As such, there is the need to study the aforementioned “unknown traffic”.

Using the traffic traces1 collected by Yajnik et al [4], we examine the loss pattern particularly the burst loss

characteristics from which we create a mathematical model to represent an ideal2 distribution of consecutive lost packets.

Next section presents the dataset from source Radio Free Vat (RFV) in California. Section III derives the ideal distributions of consecutive packet losses of the recipients of audio from RFV. Section IV details survey of related work. Finally, section V draws a conclusion.

II. DATASET FROM SOURCE RADIO FREE VAT

As the MBone escalated into explosive growth in 1995, other MBone related research activities were intensified, one of which was multicast traffic modeling. In their goal ”to examine the spatial and temporal correlation in packet loss among participants in a multicast session”, Yajnik et al [5] collected MBone traffic measurements in 1995-1996, at the time of the MBone’s explosive growth, by monitoring and recording, multicast packets which were received by participants of audio multicast sessions at 17 geographically distinct sites in the US and Europe. These multicast packets were sent from three different audio sources: the “World Radio Network” (WRN) from Washington DC, the “UC Berkeley Multimedia Seminar” (UCB) from California and “Radio Free Vat” (RFV) also from California. This section details “temporally” correlated packet losses of the dataset collected on April 19th, 1996 with source RFV. “Temporally” correlated packet loss refers to loss of consecutive packets at a given receiver.

The dataset of April 19th, 1996 was made up of the sets of received packet sequence numbers of the 12 receivers who participated in the Radio Free Vat audio multicast session. Table 1 gives the packet loss characteristics of these 12 multicast-capable hosts from different geographical locations connected via the MBone. Radio Free Vat transmitted an audio packet every 80 ms. The receivers and their locations are depicted in Fig.1, as a logical multicast tree.

A technique called “broadcast and prune” was used to create this tree. This is also known as a “reverse shortest path tree” rooted at the source. At its local network, a source broadcasted each packet. Upon receipt of the packet, an attached router sent it to all outgoing interfaces. A Reverse Forwarding (RPF) check was performed as soon as a router

1 These are the only multicast traces we are able to find Online at the time

of our investigation. 2 An ideal distribution of burst losses is one consisting of short burst losses

with very few consecutive packets.

Chin, Suk Kim, Australian Catholic University, North Sydney, School of Informatics, PO BOX 968, North Sydney NSW 2059, Australia, [email protected]

Characterizing UDP Consecutive Packet Losses in Multicast Networks

T

Third International Conference on Networking and Services(ICNS'07)0-7695-2858-9/07 $20.00 © 2007

Fig.1: Logical Multicast Tree (April 19th, 1996). It is adapted from Yajnik et al [5]. The bold lines represent the connections between backbone routers. All other lines are branches of the tree and they are on the edge of the network. • is the backbone router and • is the local LAN router.

Table 1: Receivers’ Loss Statistics (RFV April 19th, 1996). The ‘2’ and ‘3’ columns indicate that burst losses were mostly of lengths 2 to 3. The ‘ 13’ and ‘total’ columns show there were very few bursts with lengths greater than 13.

llength 1 2 3 4 5 6 7 8 9 10 11 12 13 total long spiff

5.24% 2046 115 11 0 0 2 0 0 0 0 0 0 2 130 21

ursa 5.28% 2058 118 11 0 0 2 0 0 0 0 0 0 2 133 21

lupus 5.28% 2058 118 11 0 0 2 0 0 0 0 0 0 2 133 21

float5.4% 2058 126 12 1 0 2 0 0 0 0 0 0 3 144 32

cedar 5.48% 2062 134 15 2 0 2 0 0 0 0 0 0 3 156 32

erlang 5.58% 2150 133 13 1 1 0 0 0 0 0 0 0 2 150 29

tove6.19% 2345 163 18 2 0 0 0 1 0 0 0 0 2 186 26

excalibur6.29% 2362 168 23 7 2 0 1 0 0 0 0 0 1 202 21

bagpipe6.36% 2397 179 20 3 3 0 0 0 0 0 0 0 1 206 21

edgar 12.99% 2538 217 35 10 1 1 1 0 0 0 0 0 3 278 2050

artemis 26.02% 6046 1515 368 87 25 18 61 32 18 4 0 0 3 2131 26

pax26.02% 6252 1476 358 92 27 14 70 28 8 6 2 1 4 2086 26

‘llength’ denotes loss length of a burst, that is, each of the numbers in the first row gives the numbers of consecutive packets lost in a burst. Row 2 through to the last row of the first column of the table gives the names of machines. Underneath each machine name is the overall percentage loss. The ‘ 13’ column gives the numbers of burst losses with lengths greater or equal 13. ‘total’ is the total numbers of bursts with lengths greater or equal 2. ‘long’ means the longest burst loss suffered by a receiver. RFV sent a total of 45001 packets to the set of receivers on April 19th, 1996.

Radio Free Vat (California)

spiff (Sweden)

ursa (Germany)

float (Virginia)

cedar (Texas)

erlang (Massachusetts)

pax(France)

edgar (Washington)

excalibur (California)

bagpipe(Kentucky)

tove(Maryland)

artemis (France)


received a multicast packet. This ensured that a router would only receive packets on an interface which was the most efficient path back to the source. When the packet reached a leaf router, it checked the existence of group members via IGMP queries. A prune was sent from the leaf router if it was found there were no group receivers.

To give a clearer picture of each receiver’s loss characteristics, the cumulative distributions of losses will be displayed in graphical form after a review of the probability and cumulative distributions of a random variable.

III. PROBABILITY AND CUMULATIVE DISTRIBUTION OF A RANDOM VARIABLE

“A random variable is a function that associates a real number with each element in the sample space”. [6] The set of all possible outcomes of a random experiment is defined as its sample space. A discrete sample space is one that “contains a finite number of possibilities or an unending sequence with as many elements as there are whole numbers” [6]. Therefore a discrete random variable is one with a set of countable possible outcomes. To illustrate the above definitions, a classic example of tossing a coin twice is used here. The discrete sample space of tossing a coin twice is S = {HH, HT, TH, TT}. X denotes a random variable, which is the number of heads occurring when a coin is tossed twice. Then X can assign a real number, X( ), to each outcome in the sample space, from SX = {0, 1, 2}. The outcomes of S and the corresponding values of X are listed below:

: HH HT TH TT X( ): 2 1 1 0

In this example, X( ) is a discrete random variable giving the number of heads when a coin is tossed twice.

For packet loss in the Internet/MBone, if 0 denotes a lost packet and 1 denotes a received packet, then 101 refers to a solitary/isolated packet loss. 1001 means a 2-consecutive packet loss, 10001 a 3-consecutive packet loss and so on. If X( ) is now a discrete random variable representing the number of solitary, 2-consecutive and 3-consecutive packet losses and represents the outcomes from the discrete sample space S = {101, 1001, 10001}, and the corresponding values of X are:

: 10001 1001 101 X( ): 3 2 1

X( ) is the “loss length” giving the number of consecutive packets lost in a burst where a solitary loss is a “burst loss” of length 1. That is, 111 refers to no packet loss (loss length 0), 101 has loss length 1, 1001 has loss length 2 and 10001 has loss length 3.

spiff has an overall percentage loss of 5.24%, of which 94.03% comes from solitary losses, 5.28% from bursts of length 2, 0.51% from bursts of length 3, 0.09% from bursts of length 6, 0.05% from a burst of length 17 and 0.05% from a burst of length 21. The loss lengths contributing to the loss percentages are in the set x = {1, 2, 3, 6,17, 21}. Thus x gives

all possible values of the discrete random variable X. Let f(x)be a function of the numerical values x. Then f(x) = P(X = x)represents all the probabilities of the random variable X. For example, f(2) = P(X = 2). The set of ordered pairs (x, f(x)) is the probability function or probability distribution of the discrete random variable X if, for each possible outcome x [6]:

• f(x) 0; • =

xxf 1)(

• P(X = x) = f(x).

The probability distribution of the losses of spiff is given below:

x: 1 2 3 6 17 21 f(x): 0.9403 0.0528 0.0051 0.0009 0.0005 0.0005

The cumulative distribution of the discrete random variable X with probability distribution f(x) is defined as the probability of the event {X x} [6, 7]:

F(x) = P(X x) = ≤xs

sf )( for - < x < + .

The cumulative distribution of losses of receiver spiff,computed from the actual measurement when it participated in the MBone multicast audio session from RFV (on April 19th

1996) in California, is given below:

F(1) = f(1) = 0.9403 F(2) = f(1) + f(2) = 0.9931 F(3) = f(1) + f(2) + f(3) = 0.9982 F(6) = f(1) + f(2) + f(3) + f(6) = 0.9991 F(17) = f(1) + f(2) + f(3) + f(6) + f(17) = 0.9996 F(21) = f(1) + f(2) + f(3) + f(6) + f(17) + f(21) = 1.0000.

Hence,

By plotting the points (x, f(x)), a probability distribution of spiff’s loss characteristics is shown as a bar chart (Fig.2).

0.0000 for x < 1

0.9403 for 1 x < 2

0.9931 for 2 x < 3

0.9982 for 3 x < 6

0.9991 for 6 x < 17

0.9996 for 17 x < 21

1.0000 for x 21

F(x) =


Fig. 2: Bar Chart showing the probability distribution of losses with respect to loss lengths.

Fig. 2 shows that, apart from some bursts of length 6, the dominant losses of spiff are those of length 1 and the probability of losses drops sharply from length 1 to 3, approximating a geometric distribution. The probability distribution of the geometric random variable M denoting “the number of trials on which the first success occurs”3 is given by

P[M = k] = (1-p)k-1p where k = 1, 2, … [6]

Referring to columns 2 to 4 of Table 1, we can assume MBone packet losses to have a probability distribution of geometric nature, as most of the losses concentrate on loss lengths 1 to 4. Thus, in an ideal MBone environment where there are no stray long burst losses, the probability distribution of losses with various lengths can be calculated as follows4:

• Assuming an overall percentage loss of 10%, then the probability of a 1 (successful receipt of a packet) is 0.9 and the probability of a 0 (a packet being lost) is 0.1. A solitary loss or a ‘burst’ with length 1 occurs when the probability of 3 independent events, P(101), occurs where 101 denotes 3 packets with consecutive sequence numbers. For example, a segment of spiff’s recorded sequence numbers is {110, 111, 113, 114, 115, 116, 117, 118, 119, 120, 121, 123} which witnesses 2 solitary losses in packets numbered 112 and 122. In this instance, 101 could be {111, 113} or {121, 123}. Thus, P(101) =

3 The random variable M is obtained by counting “the number M of independent Bernoulli trails until the first occurrence of a success.” [7]

4 The ideas in this section came from discussions with Dr. Michael Eckert of Optus.

P(1)*P(0)x-1 = (0.9)*(0.1) 1-1 = 0.9, as the probabilities of the 3 events, P(1), P(0) and P(1) occurring in the order 101, are independent and x =1 for loss length 1. P(1) denotes the probability of a successful receipt of a packet and P(0) is the probability of the loss of a packet. The loss length is represented by a geometric random variable M which takes on values from the set x = {1, 2, 3,…, N} where N is the longest loss length. The probability distribution of M is given by P(M = x) = P(1)*P(0)(x-1) .

• P(1001) denotes the probability of a burst loss occurring with length 2. An example of burst with length 2 is a segment with the sequence numbers {330, 331, 334, 335, 336, 337}. In this instance, {1, 0, 0, 1} is abstracted from {331, 334}. P(1001) = P(1)*P(0) (x-1) = (0.9)*(0.1) (2-1) = 0.09.

• Similarly, for losses with length 3, P(10001) = P(1)*P(0)(3-1) = (0.9)*(0.1)*(0.1) = 0.009.

• By induction, P(x) = P(1)* P(0)(x-1) where x equals the burst loss length, a value of the random variable M. Hence, an ideal probability distribution for a 5.24% overall loss is P(x = 1) = P(1)* P(0)0 =(0.9476)*(1) = 0.9476, P(x = 2) = 0.0497, P(x = 3) = 0.0026 and P(x = 1) = 0.0001.

The cumulative distribution of spiff’s packet loss characteristics, displayed in row 2 of Table 1, can be obtained by plotting the points (x, F(x)). Fig. 3 shows the cumulative distribution of the discrete random variable M that gives the loss lengths of spiff’s losses.

Fig. 3: Discrete Cumulative Distribution of spiff’s losses with “loss length” less than or equal 7.

F(x)

1.00

0.99

0.98

0.97

0.96

0.95

0.94

0.93

0.92

0.91 1 2 3 4 5 6 7

x

1 2 3 4 5 6 7 x

f(x)

1.0

0.1

0.01

0.001

0.0001


100

101

102

0.7

0.75

0.8

0.85

0.9

0.95

1

Burst loss length

Cum

ulat

ive

erro

rs

ideal: 26.02% lossartemis−g: RFV96Apr19(26.02% loss)

Fig..4: artemis’ losses are worse than the ideal case.

100

101

102

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Burst loss length

Cum

ulat

ive

erro

rs

ideal: 6.36% lossbagpipe−g: RFV96Apr19(6.36% loss)

Fig. 5: bagpipe’s losses are significantly worse than the ideal case.

100

101

102

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Burst loss length

Cum

ulat

ive

erro

rs

ideal: 5.48% losscedar−g: RFV96Apr19 (5.48% loss)

Fig. 6: cedar’s losses are significantly worse than the ideal case.

100

101

102

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

Burst loss length

Cum

ulat

ive

erro

rs

ideal: 12.99% lossedgar−g: RFV96Apr19(12.99% loss)

Fig. 7: edgar’s losses are worse than the ideal case.

100

101

102

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Burst loss length

Cum

ulat

ive

erro

rs

ideal: 5.58% losserlang−g: RFV96Apr19(5.58% loss)

Fig. 8: erlang’s losses are significantly worse than the ideal case.

100

101

102

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Burst loss length

Cum

ulat

ive

erro

rs

ideal: 6.29% lossexcalibu−g: RFVApr19(6.29% loss)

Fig. 9: excalibur’s losses are significantly worse than the ideal case.


Returning to the discussion of the receivers’ loss characteristics (source RFV dated April 19th, 1996) Figs. 4 to 9 depict the cumulative distribution of each receiver’s packet losses (from Table 1), as well as the cumulative distribution of losses without the long bursts (the ideal case such as the one generated by a geometric random variable). For each figure, the “xxx” represents the cumulative distribution of losses of the ideal case and the “ooo” depicts the cumulative distribution of each receiver’s actual losses up to loss length of 100 consecutive packets. Both the “xxx” and “ooo” are actually step functions like the ones shown in Fig. 3 but without the horizontal lines indicating where a step starts and ends.

IV. RELATED WORK

Due to the lack of ubiquitous multicast deployment, Beverly et al noted in one of the most recent multicast traffic studies that: “IP multicast traffic profiling has received little dedicated attention” [3] Most multicast measurements require “active querying network devices such as the routers. Beverly et al employed passive measurement methodology and it did not require the above-mentioned dependency. Their study presents “a complete view of the network from a service provider’s perspective as opposed to the limited view offered at the edge of the network.” Multicast packet headers were captured from “several high capacity links aggregating many commercial customers and dozens of peers to the vBNS multicast backbone.”

Almeroth’s study details the growth of MBone, as well as its usage. End user participation activities and their behavior were estimated by monitoring the captured Real Time Control Protocol (RTCP) packets [8]. Packets were captured from a session through listening for Session Announcement Protocol (SAP) advertisements and then joining the multicast group of the session that was announced.

Sarac et al [9] performed a survey on multicast active measurement and multicast tools based on SNMP. They created a multicast traffic management frame work.

Thompson et al [10] analyzed the internetMCI backbone which is now part of Cable & Wireless. Their analysis presents multicast packets as “IP-in-IP (IP protocol type 4) packets”. This is because during the time of their study, multicast traffic within the backbone was encapsulated in unicast IP. Traffic flows were expired artificially if the flows were active for over an hour. No daily pattern can be discerned in this IP-in-IP traffic study.

Handley’s [11] work detailed packet loss in a multicast tree and its variation over time. Tools were developed to log RTP/RCTP packets in order to collect mtraces from an MBone session. Pictures of multicast trees were created from the data sets.

One of the earliest studies on multicast traffic profiling, Mah’s work [12], captured a few multicast traffic traces at UC Berkeley. Mah presented total traffic bandwidth, packet rates, as well as a breakdown of traffic based on media type. By examining the volume and types of traffic transiting Berkeley’s tunnel to the MBone, his work noted the high variability of volume and distribution of IP multicast traffic and that IP

multicast conferencing traffic exhibited significant different characteristics from the conventional wide-area data traffic.

In their effort to model packet loss in the MBone, Yajnik et al [13] collected 128 hours of end-to-end unicast and multicast packet loss traces. These traces were collected by sending out packet probes at periodic intervals of 20, 40, 80 or 160ms along both unicast and multicast connections. Sequence numbers of received packets were recorded for analysis and traffic modeling.

Of the 128 hours of packet loss traces, 76 hours were identified as stationary trace segments, from which the loss process was modeled as a 2k-state discrete-time Markov chain model. When k = 0, the loss process was Bernoulli and this was accurate for 7 segments of the datasets with sampling intervals of 160ms. A 2-state Markov chain model was accurate for 10 segments and Markov chain models of orders 2 to 6 were accurate for 16 segments [13].

The above models were captured from their 1997-1998 traffic data collection from active measurements using probe packets in confined experiments targeting isolated environments. Probe packets tend to aggravate a congested network, giving rise to more packet losses, especially consecutive losses, thus resulting in inaccurate traffic characterization. Therefore, Yajnik et al’s [4, 5] MBone traffic measurements collected in 1995-1996 were used for this study. These datasets were collected through passive measurements targeting a wide region of the MBone.

Although Yajnik at al modeled one of the numerous passive measurements mathematically: “The packet loss at a receiver was modeled as a discrete-time, binary-valued Markov chain.” in their technical report [4], it was never published. Their published work on Markov models was entirely derived from the 1997-1998 active measurements. Our examination of the 1995-1996 passive measurements leads to our derivation of cumulative distributions (CDs) of multicast consecutive lost packets. These CDs facilitate characterization of multicast burst losses in an ideal communications channel where long burst losses are absent, giving “ideal” distributions of burst losses (clustering around loss lengths 2, 3, or 4, depending on the loss percentage) in multicast networks.

These CDs can be used as benchmark indicating the effectiveness of any error concealment/control techniques. Chin’s work on improving Internet video quality employed these “ideal” CDs for evaluating the efficacy of their proposed method and its variants [14]. Fig. 105 indicates that the proposed method in [14] works extremely well with receiver pax, from France, indicated by the “-.-“ and “+++” showing significantly less number of burst losses than even the “ideal” CD represented by the solid line. This is because pax has unusually high number (2086) of burst losses ranging from burst loss length 2 to 26 consecutive packets (refer to last row of Table 1).

5 This figure was displayed in WiMob2005 in August 2005 and it aroused high level of interest among some members (who asked for this work to be published) of the audience who were interested in issues in QoS in the Internet, thus providing motivation for producing this paper.


V. CONCLUSION

As multicast deployment moves towards ubiquity, multicast traffic profiling has also gained momentum, evidenced by the work presented in the above section. However, none of the work focused on the burst loss characteristics of multicast packets. Although Yajnik el al analyzed in [4, 5] the burst loss characteristics of multicast packets and called a burst loss a “temporally correlated loss”, they did not model the burst loss distribution as roughly geometric.

In this paper, firstly, we model the consecutive packet burst loss distribution of UDP packets as geometric if the unstructured events at the tail were excluded. This is consistent with previous empirical measurement of unicast packets [15, 16, 17].

Secondly, we have modeled the burst loss distribution in an “ideal” multicast network (where it is assumed to have no long stray burst losses of lengths greater than 10, say) and it is geometric, giving our so-called “ideal” cumulative distributions of multicast consecutive burst losses.

These “ideal” cumulative distributions have been used for evaluating the effectiveness of error concealment methods for improving the video quality in the Internet [14]. We will be using these “ideal” cumulative distributions for measuring the efficacy of our work in progress which is dealing with long burst losses of several hundred multicast packets in the Internet.

Further future work would be to search for more recent multicast datasets such as those in IPv6 traffic and use these to characterize the loss behavior of UDP packets .

ACKNOWLEDGMENT

I take this opportunity to thank members of the audience from the QoS group (S55 -- Mobile Networking, Video and Control) of WiMob2005. This paper has been written solely through your encouraging feedback and request for this piece of work to be published. I am also very grateful to Dr Michael

Eckert of Optus (an Australian Telecommunications Company) for giving ideas about geometric distribution. Very importantly, the reviewers of my paper (WM05-212) for WiMob2005 deserve a big “thank you” for reading and accepting my paper [14]. Thank you very much in advance to the reviewers who will be reading this paper.

REFERENCES

[1] K. Kerschbaumer, (2005, November 21). “Cisco gives Convergence a Push”: http://www.broadcastingcable.com/article/CA6285532.html].

[2] B. M. Edwards, L. A. Giuliano and B. R. Wright. Interdomain Multicast Routing: Practical Juniper Networks and Cisco Systems Solutions.Addison Wesley Professional, April 2002.

[3] R. Beverly, and K. Claffy. Wide-Area IP Mulitcast Traffic Characterization (Extended Version). CAIDA Tech. Rep., 2003.

[4] M.Yajnik, J. Kurose and D. Towsley. Packet loss correlation in the MBone multicast network. UMASS CMPSCI Tech. Rep. # 96-32, 1996.

[5] M. Yajnik, J. Kurose and D. Towsley. Packet Loss Correlation in the MBone Multicast Network. IEEE Global Internet Mini-conference, part of GLOBECOM’96), pp.94-99, London, England, November 1996.

[6] R. Walpole and R. Myers. Probability and Statistics for Engineers and Scientists. Fourth Edition. Macmillan Publishing Company, New York, 1989.

[7] A. Leon-Garcia. Probability and Random Processes for Electrical Engineers. Addison-Wesley Publishing Company, New York, 1989.

[8] K. Almeroth. A long-term analysis of growth and usage patterns in the multicast backbone. In Proceedings of IEEE INFOCOM,, March 2000.

[9] K. Sarac and K. Almeroth. Supporting multicast deployment efforts: A survey of tools for multicast monitoring. Journal of High Speed Networking, March 2001.

[10] K. Thompson. G. Miller, and R. Wilder. Wide-area Internet traffic patterns and characteristics. IEEE Transactions on Networking, pp 10-23, November 1997.

[11] M. Handley. An examination of MBone performance. Tech. Rep. ISI/RR-97-450, Information Sciences Institute (ISI), University of Southern California (USC), January 1997.

[12] B. Mah. Measurements and observations of IP multicast traffic. Tech. Rep. UCB/CSD-93735, University of California at Berkeley, March 1993.

[13] M. Yajnik, S. Moon, J. Kurose and D. Towsley. Measurement and Modelling of the Temporal Dependence in Packet Loss. In Proceedingsof IEEE INFOCOM 99, pp. 345-352, New York, March 1999.

[14] S. K. Chin. Analysis of a Method for Improving Video Quality in the Internet with Inclusion of Redundant Essential Video Data. In Proceedings of the 2005 IEEE INTERNATIONAL CONFERENCE ON Wireless and Mobile Computing, Networking and Communications, pp 204-211, August 22-24, 2005, Montreal, Canada.

[15] H. Schulzrinne, J. Kuroes, and D. Towsley. Loss correlation for queues with bursty input streams. In Proceedings of IEEE ICC ’92, pp 219-224, 1992.

[16] J. Andren, M. Hilding, and D. Veith. Understanding end-to-end internet traffic dynamics. In Proceedings of SIGCOMM’ 98, 1998.

[17] J. Bolot, S. Fosse-Parisis, and D. Towsley. Adaptive fec-based error control for internet telephony. In Proceedings of IEEE INFOCOM, pp 1453-1460, NY, March 1999.

Fig. 10. “-.-“ and “+++” indicate that the inclusion redundant base video data dramatically improves the performance of the proposed method of interleaving base packets with 2 and 4 enhancement packets, respectively. “ooo” give the CD of the actual packet losses in the MBone. The solid line gives the CD of the ideal packet loss pattern with a 26.02% loss. “***” give the CD of the packet loss pattern when the base packets have been interleaved with 4 enhancement packets. Inclusion of redundant base packet improves the method’s performance significantly for pax (from France).


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