December 2011, 18(Suppl. 2): 61�65 www.sciencedirect.com/science/journal/10058885 http://jcupt.xsw.bupt.cn

The Journal of China Universities of Posts and Telecommunications

Measurement-based peer selection for P2P-IPTV services in

campus networks WANG Zhen-hua1 (�), HUO Yu-song2, LIU Peng3, FAN Zi-wen4, MAYan1

1. Network Information Center, Institute of Networking Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

2. China Unicom Research Institute, Beijing 100032, China 3. School of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300160, China

4. School of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract

P2P-based IPTV has been a gradual hot topic both for the academic institutions and service providers, which offers residential and commercial users digital television services through IP protocol and media sharing among peers at low cost. A rational peer selection algorithm plays an essential role on offering reliable media streaming transmission and excellent user experience. Meanwhile, convenient and effective quality of experience (QoE) assessment mechanism is not only an important evaluation index for IPTV service providers, but also can supply high-efficient performance feedback for them, especially under a limited network resource environment. Our contributions in this paper are three-fold: 1) we confirm the key parameters which affect QoE among P2P live application according to our 6-month trace, and establish a rational peer selection algorithm by virtue of the parameters above; 2) we set up a novel and passive model for QoE assessment representation; 3) during the popularization in campus networks, we approve the effectiveness of our new QoE evaluation method and the improvement of users’ video quality caused by peer selection strategy. Whether or not, we hope our work can supply reliable references for IPTV service providers, moreover, actual improvement for future IPTV deployment.

Keywords P2P, IPTV, media streaming, measurement, QoE

1 Introduction�

IPTV consists of several components, which uses IP protocol to deliver multicasting TV, video on demand (VoD), triple play, VoIP, etc. through broadband connections. For the purpose of media distribution, IPTV services combined with P2P streaming technique is widely accepted by more users due to its less hardware cost. Meanwhile, the independence of publication servers can help more users participate in the sharing and transmitting media streams, obviously which lowers the heavy load at server-ends. Currently, both Internet service providers (ISPs) and common users give much attention to P2P-IPTV, and QoE from the users’ point of view is the most important evaluation for IPTV service providers.

In terms of the streaming chunk scheduling algorithm, P2P-based IPTV can be divided into two categories, pull and Received date: 18-11-2011 Corresponding author: WANG Zhen-hua, E-mail: wzhdl@bupt.edu.cn DOI: 10.1016/S1005-8885(10)60147-1

push respectively. Push scheme can be applied in campus and residential networks since the main characteristic of P2P live streaming is to deliver synchronized media data to all the online users as fast as possible.

From the perspective of end-users, a well-selected media server or peer from which to receive media streaming could bring obvious improvement of users’ experience, such as start-up delay, channel switching delay, content continuity, etc. It’s not enough only relying on the application-layer metrics to finish the candidate neighbor adjustment procedure because most of the higher level factors are directly determined by under-layer real network parameters. Therefore, it’s essential to take network-layer measurement results, along with application-layer metric into the decision-making algorithm.

In addition, QoE is considered “a measurement of end-to-end performance levels at the user’s perspective and an indicator of how well this system meets the user’s needs” [1]. Subjective evaluation and objective evaluation are two common and different methods to evaluate and measure the

62 The Journal of China Universities of Posts and Telecommunications 2011

video quality delivered to the end-users. Subjective evaluation is well-known as the scoring process that comes from lots of subscribers’ feelings of the streaming performance [2]. The scalability of subjective evaluation is a little worse due to the participation of abundant users. On the other hand, objective evaluation takes multiple parameters from several layers into consideration, and the candidate parameters generally include start-up delay, channel zapping time located in the service layer, frame loss during the encoder and/or decoder progress at the application layer, and transmission delay, packet loss rate at the network plane. Although objective method cannot reflect users’ perception directly, it’s widely recognized for its operability and expansibility.

The structure of this paper is as follows. In Sect. 2, we illustrate the related work of the QoE evaluation and our motivation according to the existing works in P2P-IPTV services. In Sect. 3, we describe the key parameters which affect QoE in P2P live applications according to our 6-month collection and we put forward peer selection algorithm based upon analysis measurement results. In Sect. 4, we scheme the candidate metric model for QoE representation and confirm the effectiveness of our passive QoE evaluation which is examined by real deployment. In conclusion, we arrange a series of future works in Sect. 5.

2 Related works

2.1 QoE assessment in P2P applications

As mentioned above, widely applied QoE assessment mechanism is carried out in objective pattern. Under such a circumstance, not only network transmission performance can influence users’ experience, but also the capacity at the publication server can play a significant role in estimating IPTV service quality. Whether considering media streaming source as a reference for QoE comparison and assessment, on this account, there exists three approaches applied in real life, full reference pattern, reduced reference pattern and no reference pattern.

Full reference pattern determines video quality through calculating the input video signal at the source of IPTV system and the received media signal at user end; however, full reference costs much along with its high-accuracy. In despite of disadvantages of full reference pattern, reduced reference pattern just selects partial parameters to accomplish the similar work. Besides, no reference pattern only calculates the received video signal to evaluate video quality.

Media delivery index (MDI), a common used method, also is an objective and passive measurement method, which cannot cause the most accurate results, but provide a representation for the given media streaming. MDI is

documented as the whitepaper in Ref. [3] that utilizes delay factor (DF) and media loss rate (MLR), which is considered as the indicators of the quality of IPTV services. As definition of the three patterns, MDI is a typical no-reference pattern.

In generally, for QoE, a better metric model is the most important beginning while deploying IPTV service in large-scale network environments such as campus networks or residential accesses. Due to influence from the network performance on user experience, a large number of cross-layer metrics should be well collected and analyzed for the latter QoE modeling expression and a reference-aware pattern will be the feasible improvement of traditional evaluation.

2.2 Motivation

So far, academic researchers have deployed many measurements and analysises for mesh-pull P2P system on the Internet [4–5], most of which involve different aspects from network packets tracing to application level behaviors, including the number of users, arrival and departure patterns, and peer geographic distributions [6–7]. All of these above can give a trustworthy reference to help ISPs measure the services and network performance to some extent, but QoE evaluation based upon real traffic in world-wide IPTV system is rarely mentioned.

Referring to the successful experience of the previous researches, to obtain a better understanding about characteristics with tree-push mechanism is the goal of our principal work in P2P-IPTV live applications. We have studied an in-depth measurement of one of the single-source tree structure prototypes, namely PeerCast [8] in order to get insights of tree-push P2P-IPTV systems and the traffic loads they place on ISPs. PeerCast is an open source media streaming tool, which can supply convenient way to add more functions for information gathering. We modify the original PeerCast source code to support multiple parameters collection. Depending on the advantage of easy access to both the CERNET [9] and NSFCnet [10] which is offered by our lab, decades of probes are deployed in the campus and a trace of PeerCast traffic for more than 6-month is captured by several approaches. In total, over 300 000 measurement records are well classified and analyzed from Oct. 2009 to Apr. 2010.

3 Measurement-based peer selection algorithm

As we have defined in the previous section, a comprehensive peer selection algorithm and QoE indicator contains multiple parameters from different layers. However, for service layer metrics, such as start-up delay and channel

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zapping time, it’s difficult to collect since these parameters are usually in relation to the terminal performance and buffering mechanism. For example, a central processing unit (CPU) with low-capacity may spend much time decoding the media chunk, which enhances start-up delay. Similarly, the bigger buffer size can also lead to less perceptive from the subscribers for the channel or peer switches. On the contrary, it’s much easier to collect application layer metrics, like encoding rate and frame setting, etc.

At the same time, we never ignore network parameters which have great influence on the end-to-end performance in IPTV services. Instead of packet loss rate, one-way time delay (OWTD) and bandwidth are taken as the senior metrics under test. Detailed explanation for our measurement work is presented in Ref. [11], so we just illustrate several figures and conclusions in brief here.

Fig. 1 Bandwidth consumption and coding rate

Recall that our candidate metric model for QoE representation is the reference-aware pattern, so to consider the quality of source encoding rate as a part of the QoE assessment is essential. In Fig. 1, we can conclude that the relationship between encoding rate and bandwidth consumption is the approximate linear change in Eq. (1) of cross-layer parameters, where Rc is short for encoding bitrate, Bc(t) and K(t) represent the time-shifting bandwidth consumption and slope variation respectively.

c c(t) (t)� �B K R (1) As the same treatment while analyzing the mapping relation

between encoding rate and bandwidth consuming, several testing topologies have been set up for available bandwidth measurement in various network access conditions, including local area network (LAN), virtualized sharing networks and cross domain traffic traced between CERNET and NSFCnet. According to our measurement statistics, the available bandwidth of any participant in the IPTV service is also indicating the decreasing linear tendency which is summarized as Eq. (2). Bmax stands for the narrow link bandwidth of a given transmission path, and N is the acceptable relaying number under such as a connection circumstance.

max c max c( ) ( ) ( )� � � � � � �aB t B N B t B N K t R (2) In addition, as ITU-T Y.1540 [12] showed, QoE can be

affected by the OWTD between server and client. So, OWTD in the application layer multicast (ALM) tree structure was

also measured for preliminary analysis. Limited to the space constraint, detailed description could be found in our previous conclusion [11].

Depending on the long-term measurement for cross-layer parameters and analysis for mapping relation, we confirm the peer selection algorithm which is suitable for media streaming live application in campus and regional networks. Before presenting an exact formulating definition, it’s better to conclude measurement and analysis the works above in brief as all of these conclusions and considerations play a key role on defining candidate peers:

(a) According to statistical analysis for encoding rate and bandwidth consumption, and the measurement results of available bandwidth, we can consider that bandwidth factor must be a leading parameter to affect media streaming distribution quality, especially to apply high-definition and high-bitrate streaming distribution through single-source ALM tree structure.

(b) Although the correlation of delay variation and hop number is not obvious in the existing result, the probability of topology reconstruction would increase obviously which is caused by a mass of peers frequently churning in P2P environment accompanying with hop number increasing, especially in sing-source ALM tree structure, since the natural attribute of this structure is that it only can get media chunk from the only parent peer.

(c) The degree of an relaying peer indicates its current load and service capability, which can offer certain guidance to peer selection algorithm. Based upon the above conclusions, we define the weighted

model for performance of assessment candidate peers, as follows: 1 d 2 c 3 v� � � � � �W w N w H w A (3)

As we can see, Nd means the real network distance between candidate peers and current requesting peer, which is estimated by IP address information of peers, with inversely proportional to the real router hop-counts, if both of the peers locate in the same LAN, the value Eq. (1), the definition of application-layer hop-count proportion Hc is showed in Eq. (3) where h denotes the depth in application-layer tree structure for candidate peers, the availability is defined as follows:

v# of relays

max relays constraint�A (4)

The assignment of each weighted value wi in the model is in line with user preference concept, and in our experiment, the weight of each dimension parameter is 0.7, 0.2 and 0.1 respectively.

4 QoE evaluation and experiment results

4.1 Testbed description

High-speed access network that connect to both CERNET and NSFCnet is available to setup the testbed and IPTV

64 The Journal of China Universities of Posts and Telecommunications 2011

service popularization in our university. Therefore, the full measurement and assessment work is carried on under such a network environment.

Fig. 2 Illustration of one test topology in a campus network

Fig. 2 presents a simple test example during our real work. Most of the relay nodes are distributed in two important parts of the campus network, that is teaching area (TA) and the students’ dormitory network (Dorm). Since the interconnection in CERNET seems unlimited in terms of streaming transmission, we also allocate several listeners in NSFCnet to capture the cross-domain performance where bandwidth constraint happens. Publishing server is set in the backbone to providing better access and connection abilities to all the listeners. Of course, the virtual topology constructed by all the participants forms an ALM tree where the root of the tree is our media source server.

4.2 QoE assessment modeling and implementation

In order to keep pace with QoE assessment method for media streaming applications that MDI applies, we suggest MLR as one column in the combine expression, and exploit the complementary parameter called media receiving rate (MRR) to calculate the amounts of receiving bits from the connection socket at a given interval. The expression of MRR is displayed as Eq. (5), where R(t) means the total bits received at any time stamp. Moreover, our IPTV QoE assessment works in reference-aware pattern which means comparison between the receiving media quality and the source streams. Therefore, we define a metric named media sending rate (MSR) to examine the quality from the publication server, which can be equal to Bc in Eq. (1).

( ) ( )MRR( ) lim R t t R ttt t

� � ��� �

(5)

c cMSR( ) ( ) ( )� � �t B t K t R (6) According to these two metrics and previous network

parameters, the definition of our IPTV QoE assessment index is denoted as the following tuple:

( ), ( ),DVQ t K t (7)

The middle element K(t) is mentioned above, and DV is short for IPDV which defined in Ref. [13]. Q(t) is the normalization result from MRR and MSR, namely MRR is divided by MSR. Generally speaking, it’s not easy to accurately calculate MRR with measurable methods. Instead, we utilize sampling and statistics to accomplish this purpose.

4.3 Experiment results

The scenario in Fig. 3 is randomly selected from databases which show the sampling results of MRR and MSR. The sample interval, �t, is set as 5 s and Q(t) is calculated as follow.

mean value of MRR( )( )mean value of MSR( )

tQ tt

� (8)

Fig. 3 Sample of MRR and MSR

It is obviously that the user in Fig. 3 suffers the worse perception with low MRR and Q(t) values caused by media packet loss. Some intermediate data and evaluation records are listed in the following table.

Table 1 Statistics results for QoE evaluation

Sampling data of MSR

/kbit s-1

Sampling data of MRR

/ kbit s-1

Mean value of

MSR /kbit s-1

Mean value of

MRR /kbit s-1

Q(t)/(%) K(t)/(%)

618.9682

669.4

461.88 444

435.1

669.4 452.9 67.658 98.877

At the same time, we modify anther PeerCast edition to

support depth-first peer selection mechanism, moreover, randomly select several peers from statistic results to do comparison testing. The testing result is as stated in Figure 4.

Shown in Fig. 4, for most of peers, the new peer selection algorithm can universally improve the media streaming quality for users in live applications, so as to gain higher degree of satisfaction and experience.

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Fig. 4 Q(t) value comparison of our QoE evaluation model

Meanwhile, with the help of deployment and utility in campus networks, we confirm the effectiveness and convenience of our QoE evaluation mechanism. With a little additional information added into exchange information among peer nodes, namely Q(t) and K(t) values, this kind of evaluation method possesses strong expansibility. If both Q(t) and K(t) have high values, the performance of distributing would be excellent. However, with low values of Q(t) and high value of K(t), we have to re-select relaying node for current user or reconstruct the topology of the ALM tree. The third case, the low value of K(t) stands for the poor quality streaming provided by media source.

5 Conclusions

To briefly summarize our contribution, firstly we measure and analyze enough cross-layer parameters which can affect QoE in P2P-IPTV services. On the basis of the statistics results and relationship mapping, the key metrics for peer selection procedure is carried out. Then, we model two key parameters of QoE assessment which are in line with the common MDI description. Simultaneously, we define the tuple for our QoE index, which can be easily extended to a related large-scale service network and works in an objective, passive and reference-aware pattern. At last, we demonstrate how to apply QoE assessment method for real life, which owns a possible and convenient qualification approaching the calculation of quality rate.

But to be honest, there are still some aspects we can make them better, for instance, only CBR technique is utilized to offer the entire IPTV system live satellite TV signals and movie plays at this moment, it’s possible to employ various

audio / video coding rate for different access network. The environment for testing network is not ripe, also, the application scenario mainly cares about the IPTV live applications, but there exists further research to enhance the QoE assessment for VoD operations in heterogeneous networks. Whether or not, hopefully, our research work can make some contributions for the development and deployment of IPTV services in future.

Acknowledgements

This work was supported by the National Basic Research Program of China (2009CB320505), the National Science Foundation of China(61003282), International Scientific and Technological Cooperation Program(S2010GR0902), the Fundamental Research Funds for the Central Universities(2011RC0508), the Major National Science and Technology Special Project (2010ZX03005-003) and National Hi-Tech Research and Development Program of China (2011AA010704).

References

1. Goodchild J. Integrating data, voice and video–part II. IP Video Implementation and Planning Guide, United States Telecom Association, 2005

2. Itu R. Recommendation BT.500: methodology for the subjective assessment of the quality of television pictures. June, 2002

3. Scalable IPTV quality testing. Agilent Technologies, Whitepaper, 2005 4. Tewari S, Kleinrock L. Analytical model for BitTorrent-based live video

streaming. IEEE NIME 2007 Workshop, Jan. 2007 5. Wu, Li B, Zhao S. Characterizing peer-to-peer streaming flows. IEEE

Journal on Selected Areas in Communications, 2007: 1612�1626 6. Hei X, Liang C, Liang J, et al. A measurement study of a large-scale P2P

IPTV system. IEEE Transactions on Multimedia, 2007, 9(8) 7. Silverston T, Fourmaux O, Botta A, et al. Traffic analysis of peer-to-peer

IPTV communities. Computer Networks, 2009, 53: 470�484 8. PeerCast. http://www.peercast.org 9. CERNET. http://www.edu.cn/english_1369/index.shtml

10. NSFCnet. http://www.nsfcnet.net 11. Huo Y, Su Y, Wang Z, et al. Design of cross-layer model for QoE

enhancement in P2P live applications. ICCASM, Oct, 2010 12. ITU-T recommendations Y1540. Internet protocol, data communication

service-IP packet transfer and availability performance parameters, 1999 13. IETF-RFC 3393. IP packet delay variation metric for IP performance

metrics (IPPM), 2002