DEPLOYMENT OF LIVE-VIDEO SERVICES BASED ON

STREAMING TECHNOLOGY OVER AN HFC NETWORK

David Melendi

, Xabiel G. Pañeda

, Roberto García

, Ricardo Bonis, Víctor G. García

Computer Science Department, University of Oviedo

Campus Universitario de Viesques. Sede Departamental Oeste, 33204 Xixón-Gijón, Asturies

H H

Keywords: Live, Video, Streaming, Multimedia, HFC.

Abstract: This paper presents an approach to the deployment of a live-video service based on streaming technology

over an HFC network. This approach covers most of the issues that may arise while putting one of these

services into operation, taking into account new aspects such as those oriented to the improvement and prior

analysis of the service’s behaviour. An accurate and continuous service analysis can contribute to boost the

service’s performance and thus to lead the service to the so called excellence of service. This paper also

presents a service architecture specifically designed for HFC networks that takes advantage of the structure

of this kind of networks. Furthermore, a complete framework that facilitates most of the tasks that are

needed to deploy and manage a live-video service over the internet is presented.

1 INTRODUCTION

The emergence of the World Wide Web has changed

the Internet world. This service has become a

powerful medium. Daily, an important number of

web accesses is produced and a huge volume of

information is delivered. The bandwidth increase in

subscribers’ access capabilities has given rise to the

appearance of a new complementary service: the

Internet video. There are two types of video services

on the Internet: live-video and video-on-demand. In

video-on-demand services, the user requests the

information at any time and the server delivers it

exclusively. This system allows users to interact

with information: Pauses, backward and forward

jumps are allowed. Its behaviour is similar to a

videotape. On the other hand, in live-video services,

contents are received directly by the server, which

broadcasts them straight out to the audience.

Nowadays, most video services on the Internet

are based on streaming technology. The advantages

of video streaming and the subscribers’ expectations

are important. However, this technology presents

some problems. Video delivering consumes an

important bandwidth in the network and requires a

constant quality of service. What is more, live-video

services require much more transmission capabilities

than video-on-demand services, due to the fact that

all the users connect at the same time. To maintain

service quality under control and select the most

interesting contents, the use of proper engineering

techniques and good analysis methods is

fundamental. The analysis systems must provide the

necessary information to ensure the correct

configuration of the streaming service, and take as

much advantage as possible of the subjacent network

technology.

In this paper, an approach to engineering and

analysis methods for live-video services over HFC

networks is presented. The main aim of this work is

to provide useful tips to help service managers in

planning, deploying, configuring and improving

live-video services. Furthermore, the paper has

followed an interesting practical approach, based on

the improvement of these services through the

analysis of the information provided by existing

technologies.

The improvement in the transmission of

multimedia contents over the internet is a fact in the

current research world. There are abundant papers

that cover most of the topics related to the

technologies involved in the distribution of live-

video contents. Some of them, such as (Chow, 2000)

or (Turletti, 1994) commented on new engineering

techniques to deploy live-video services, but

assuming the availability of multicast technologies.

Others like (Ortega, 2000) or (Tham, 2003), are

mainly oriented to the study or the development of

new data formats for the transmission of live-video.

There are others such as (Chawathe, 2000),

256

Melendi D., G. Pañeda X., Garcia R., Bonis R. and G. Garcia V. (2004).

DEPLOYMENT OF LIVE-VIDEO SERVICES BASED ON STREAMING TECHNOLOGY OVER AN HFC NETWORK.

In Proceedings of the First International Conference on E-Business and Telecommunication Networks, pages 256-263

DOI: 10.5220/0001383802560263

 SciTePress

(Deshpande, 2001), (Nguyen, 2002) or

(Padmanabhan, 2002), that offer different

approaches to the deployment of a streaming service

over a network, but using proprietary solutions or

basing their research on service models or

simulations.

Although some of the topics covered in this

paper have been revised in other publications, the

main difference is the practical point of view that

has been followed. The conclusions have been

obtained through the analysis of the available data

from one of these services and the solutions have

been designed to improve a real service.

The rest of the paper is organized as follows:

Section 2 shows the proposed service architecture

over an HFC network. A detailed explanation of

live-video services engineering methods is set out in

section 3. Section 4 covers an approach to live-video

services analysis. An introduction to service

configuration is offered in Section 5. Finally,

conclusions will be presented in section 6.

2 SERVICE ARCHITECTURE

A live-video service requires the installation of

several devices to support the content distribution

over the network. The main components that any of

these services need are the production software, the

streaming server, a set of proxies and the multimedia

clients that should be installed in the customers’

computers. The distribution of these devices over the

network is clearly connected with the type of

networking technology that is being used, and the

future performance of the service will be determined

by the placement of each of the systems involved.

Figure 1 shows the proposed distribution over an

HFC network.

HFC networks are commonly structured

hierarchically around a central spot that delivers all

the services to the users that are connected to the

network (García, 2003). Although the physical

structure of most of these networks may seem

different due to the use of ATM backbone rings or

other redundant architectures, the logical structure is

always hierarchical around this central point, called

the head end. The head end manages all the services

in a centralized way: the accesses to the internet, the

compilation and distribution of the TV channels, the

connection to the telephone networks, etc. It is also

in charge of assigning the proper resources to the

users whenever they try to use one of the services

provided. Therefore, the best place to install the

streaming server, whose mission is to deliver

contents to the users, is precisely close to the head

end. This location will permit both a better

management by the owner of the network and an

increased assignment of output bandwidth rate, in

order to avoid problems while distributing the

contents to the network.

On the other hand, the mission of the production

software is to capture live or stored contents, adapt

them for streaming transmission, and deliver them to

the streaming server. This device should be as close

to the streaming server as possible, in order to avoid

cuts during the transmission of contents between

both systems. If the contents are being captured live

in a remote location with access to the HFC

network, a proper constant bit-rate connection

should be allocated to preserve transmission quality.

If that location is outside the HFC network, two

alternatives need to be considered: either to

subcontract an external connection, or to store the

Figure 1: Service Architecture

DEPLOYMENT OF LIVE-VIDEO SERVICES BASED ON STREAMING TECHNOLOGY OVER AN HFC NETWORK

257

contents and retransmit the saved files later. If an

external connection is subcontracted, the external

provider must guarantee transmission quality on the

route between the producer and the streaming server.

The optimal type of connection for live

transmissions is a multicast connection, which

reduces the amount of traffic in the network and the

load on the server. But multicasting can not be used

in most of the existing networks due to hardware

incompatibilities, so proxies could be used in order

to improve network performance. The mission of

proxies is to receive multimedia streams from the

main server and retransmit them to final customers

or to other proxies. Furthermore, the use of this kind

of devices may reduce the load on the streaming

server, and avoid possible cuts during the

transmission of contents due to a hypothetical

overload of that machine. Proxies can also be

installed following an on-cascade service

architecture. This architecture allows proxies to

serve contents to other proxies, acting as servers,

and reduces the load on the main server.

Every heavy-loaded branch of the HFC network

should have, at least, one proxy running in order to

serve the customers in that branch. If one proxy is

not enough to serve one of those branches, more can

also be placed following the on-cascade architecture

mentioned before. On the other hand, branches with

a small number of users can be served from a remote

proxy, possibly allocated at the head end with proper

connection capabilities. If the contents are also

going to be delivered outside the HFC network, an

additional proxy could be placed to attend all the

requests coming from the Internet.

Should the service be offered to external

connections, it is also important to consider the

placement of several proxies in the networks used by

potential users, through some kind of service level

agreements with the corresponding access providers.

3 SERVICE ENGINEERING

The deployment of any high-cost service that may

suffer problems due to several different

circumstances, requires an intense development of

engineering tasks in order to reduce service costs,

improve service performance and increase customer

satisfaction. These engineering tasks should be

oriented to improve the service in the following

areas: the network, devices and contents.

The network is a critical aspect in any distributed

service. It is even more critical in services like live-

video distribution, where contents need to be sent

with a constant rate to avoid cuts during their

reproduction in the customers’ computers. Although

the optimal network design for these services is not

always available, the use of some alternative

solutions may mitigate most of the transmission

problems that can arise during the delivery of

contents. In most cases, transmission difficulties

appear in the network’s segment known as last-mile.

One of the features of HFC technology is that it

combines optical fibre and coaxial cable

infrastructures, relegating the latter to the last

extreme of the network, shared between 100 and 200

customers. The fact that these network extremes

work under a best effort strategy, combined with the

limits of the coaxial cable, reduces transmission

capabilities and the network’s grade of scalability. If

there are a high number of users that demand the

transmission of live-video contents in one of these

extremes, the only way to avoid transmission

problems is to bring the optical fibre closer to users,

or to reduce the number of users that can be

connected to the network in those extremes. It is

clear that these solutions are not always feasible, so

the only way to deliver live contents to those users is

to produce them with a decreased video quality.

There are also technologies available in the

market designed to ensure the content delivery, such

as surestream (RealNetworks, 2002). This technique

is capable of adapting contents’ quality in real time,

depending on the transmission capacity that is

perceived in the customers’ computers.

To detect transmission problems it is necessary

to analyze the network’s behaviour, and both the

server and proxies log files in order to identify late

arrival of packets, disorder of packets, loss of

packets, reduced reproduction times, etc. Figure 2

shows requests with delivery incidences registered

during a real live event.

Although not common, there are sometimes

other problems produced in the network due to

incorrect routing configurations that may produce

the loss of packets or their late arrival. The existence

of this kind of problems may affect not only the

transmission quality of live-video, but also the

Figure 2: Requests with delivery problems

5012

353

217

1000

2000

3000

4000

5000

6000

Requests

Total With Disorders With Loss es

ICETE 2004 - WIRELESS COMMUNICATION SYSTEMS AND NETWORKS

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quality of all the delivered data. Although the

detection of this type of errors is even harder than in

the previous case, a high loss packet rate or late

packet rate of the customers of a determined

network branch, can be the definitive clue to identify

incorrect routing policies in the network. Again, the

solution to this problem can be found through the

analysis of the server and the proxies log files.

Moving forward, the second issue that was

expressed as critical was related to the devices that

are being used during the transmission of live

contents. The simpler live-video service consists of a

machine where both the production and the

distribution software are running. This initial

configuration may suffer several problems such as a

high CPU load, huge memory consumption, elevated

hard disk utilization and a possible overload in the

output connection to the network.

The execution of both programmes in the same

machine may overload the computing capacity of the

latter, and so affect service performance in a severe

way. It must be taken into account that as users’

requests reach the server, higher resources are

needed to maintain service quality. It is necessary to

observe CPU load and memory consumption in

order to detect performance problems in this kind of

services. If overload errors occur, an inexpensive

investment is to dedicate one machine to produce the

contents and another to host the streaming server.

This new configuration requires a high

connection quality between both devices. If a direct

or dedicated connection is not possible, it is essential

to analyse the producer’s log files to detect problems

that may arise during the delivery of contents.

It is necessary to comment that there are some

connection policies used in commercial applications

that do not report about transmission problems

between the producer and the server. An example is

one of the push methods provided by Realnetworks’

Helix Producer, where streaming servers do not

establish a feedback channel with the producers.

Special care must be taken in these cases, and other

connection methods should be used if quality can

not be assured. As far as the connection method is

concerned, this will depend on the distribution and

the number of connections that the server receives. If

there is a constant connection rate in the server, one

of the available push methods should be used. On

the other hand, if there is a variable arrival of

requests, a pull connection may be the best solution

to save resources in both machines.

Although the split of production and delivery

applications between two computers is a clear

improvement, a high connection rate in the server

may cause the previously commented overload. If all

the requests are attended by a single machine,

several problems may again be encountered: high

CPU utilization, memory overload, elevated

bandwidth consumption, and license limitations.

Commercial licenses usually affect the number

of simultaneous connections, or the output

bandwidth that servers can handle. If delivery

problems are being caused by license restrictions,

the simplest solution is to acquire a less restrictive

license. To detect this type of problems, it is

necessary to observe the server’s log files, calculate

all the simultaneous connections that are being

handled in every moment, and compare them to the

number of simultaneous connections that are

permitted by the existing license. It is also necessary

to calculate the output bandwidth that is being used,

and compare it with both the license limitations and

the capacity of the line that is being used to deliver

the contents to the users. If there is high bandwidth

consumption in the server’s output, network

reengineering must be carried out in order to

mitigate these problems. More capacity should be

allocated, or clustering solutions should be applied

by distributing several proxies in the network that

will support the delivery of contents to the users.

The latter solution is also applicable when

performance problems have been detected in the

machine that hosts the streaming server, and a

computing capacity increase is not feasible.

Proxies are in charge of forwarding the contents

to the users. Although in on-demand transmissions

they operate following caching strategies, in live

transmissions they mainly receive the streams sent

by other devices and forward them to the users that

request the contents. The origin devices could be the

main server or another proxy that works under an

on-cascade architecture.

In networks where multicasting is not available,

proxies can be used to bring the transmission closer

to users, reducing the load on the main server and

decreasing traffic in the network. In HFC networks,

proxies could be allocated in heavy-loaded branches

where there is an important number of users

requesting the transmission of contents. A step

45%

29%

19%

Int ernal External 1 External 2 External 3 Other

Figure 3: Origin of Requests

DEPLOYMENT OF LIVE-VIDEO SERVICES BASED ON STREAMING TECHNOLOGY OVER AN HFC NETWORK

259

forward is to install several proxies on-cascade,

depending on the evolution of demand in those

branches, or in the load that has been registered in

the proxies. On the other hand, network branches

with a low number of requests could be served

directly from the main server, or for further

performance, from a centralized proxy that could be

used to redirect transmissions to external networks.

In any case, it is very important to collect data from

the network and the proxies that have been installed,

and analyze said data in order to detect possible

performance deficiencies or loss in transmissions.

Figure 3 shows the origin of users’ requests,

registered during a real live event

An extremely important issue is that of content

management. Contents are usually provided by a

different entity than the network operator.

Sometimes it is a communications media, such as a

TV company or a digital newspaper, other times a

movie producer, and most of the times a media

management company that sells contents to other

businesses. Once those contents are delivered

through the network, it is very important to analyze

whether they have been successful or not. An

inadequate selection of contents may greatly

influence the budget of the service or its

profitability. Although it is very difficult to calculate

audience statistics in other services such as

conventional TV, with live-video transmissions it is

possible to obtain detailed information about users’

accesses. There are different aspects that could drive

the production of contents, and are available in this

kind of services: number and length of connections,

preferred time ranges, users’ installed language and

computing capacity, etc. These are very important

data that should not be underestimated. Servers and

proxies log files provide this type of information that

needs to be analyzed in detail in order to calculate

user’s satisfaction and preferences. This information

is usually owned by network operators, who could

give consultancy support, or reporting services to

content providers.

4 APPROACH TO SERVICE

ANALYSIS

Once the service has been deployed over the

network, it is necessary to monitor the transmissions

and check if everything is working properly. It must

be taken into account that live-video services do not

allow second chances, after they occur their live

transmission is no more interesting. Other services

such as video-on-demand could be improved using

continuous analysis and configuration cycles, but

live-videos are slightly different due to their

temporary nature. Errors during a live transmission

are complete failures, so everything must work

properly to ensure the success of the service.

Although live-video transmissions with problems

can not be fixed, their analysis can be considered as

a continuous learning tool to improve future

emissions. The traditional learn through experience

thesis is perfectly applicable to these services. So it

is necessary to analyze live-video transmissions to

know what is happening, why it is happening and

how it can be improved.

The analysis of live-video services consists of

the detailed observation of three of the different

stages that can be identified in any live transmission:

production, distribution and visualization. Hence the

division of service analysis in the following phases:

Production Analysis, Distribution Analysis and

Visualization Analysis. At the same time, these three

analyses consider the issues that were laid down in

the previous section –network, devices and contents-

from different points of view.

4.1 Production Analysis

Production analysis is centred on the contents

production phase. During this stage, the contents are

captured and coded using a particular algorithm.

After digitalizing contents in the proper format, they

are sent to the server using the streaming

technology. It is necessary to ensure that the device

that is in charge of this task does not suffer any

performance incidence. It is also very important to

check the connection between the producer and the

streaming server. Among others, such as CPU

throughput, or memory consumption, the following

quality metrics can be used for the analysis of

production phases: Production Loss Rate and

Production Bandwidth Consumption.

Production Loss Rate calculates losses in the

transmission channel between the producer and the

streaming server. It can be obtained through

equation 1.

SRRP

PLR

−

Eq. 1

Where RP is the number of resent packets, SR

the amount of successful resends and PS the number

of packets sent to the streaming server. All this data

can be gathered from the producer’s log files.

This metric is designed to calculate the losses of

information during the production phase, generated

by problems in the connection between the

production software and the streaming server. It

must be taken into account that, although some

transmission problems can be mitigated thanks to the

input buffer allocated in the streaming server, severe

ICETE 2004 - WIRELESS COMMUNICATION SYSTEMS AND NETWORKS

260

conditions in the connection between both devices

can mean an important decrease in the quality of the

service. Should these problems appear, an

improvement in the network infrastructures needs to

be requested in order to guarantee a constant

transmission quality to assure the delivery of

contents to the streaming server.

Production Bandwidth Consumption calculates

the bandwidth that live production is consuming. It

can be obtained using equation number 2.

AVB

TBR

PBC =

Eq. 2

Where TBR is the total bit-rate generated in the

production phase, and AVB is the available

bandwidth in the output of the production device.

The first parameter is obtained from the producer’s

log files, adding the output quality that is being

generated for each of the targeted audiences of the

service, whereas the latter is the bandwidth that is

available in the connection where the production

device has been plugged into. It is obvious that AVB

can never be less than TBR, because this situation

would lead to an increase of the losses in the channel

between the production device and the streaming

server. Moreover, it must be taken into account that

other applications running in the production device

may consume output bandwidth, so PBC should

never be greater than 0.75.

On the other hand, there is no available

information in this phase to analyze contents. But it

must be taken into account that the media selection

is closely related to the analysis of the users’

preferences. So this phase depends entirely on the

results obtained in the Visualization Analysis phase.

4.2 Distribution Analysis

Distribution analysis is designed to control the

quality of the transmissions established between the

main streaming server, the proxies and the final

customers of the service.

Each of the devices that need to be used to

deploy a live-video service over an HFC network,

need to be analyzed in detail, to detect performance

issues that may affect the final results of the

transmission. Hence, it is necessary to analyze the

evolution in the resources’ consumption of those

devices: CPU utilization, memory load, bandwidth

consumption, etc. These devices are usually owned

by network operators, so no transmission limitations

have been considered, except those inherent to the

HFC technology and the available network

infrastructures. Apart from the typical performance

analyses, it is also necessary to consider the license

consumption in the main streaming server and the

proxies spread throughout the network.

These licenses usually limit the number of

concurrent connections accepted by each device, or

the output bandwidth that is being dedicated to

deliver multimedia contents. It is important to

mention this feature, because it can severely damage

the growth of the service, rejecting connections

requested by new users. The licenses utilization can

be obtained calculating one of the equations 3 or 4.

MBR

TBR

TLC

MAC

ULC ==

Eq. 3 and 4

Where ULC is the users’ license consumption,

CC is the number of current connections, MAC is the

maximum accepted connections, TLC is the

transmission’s license consumption, TBR is the total

bit-rate used to deliver the contents, and MBR is the

maximum bit-rate accepted. If ULC or TLC reach 1

during long periods of time, it is necessary to

consider the acquisition of a higher license. Figure 4

shows the evolution of TBR in the output of a

streaming server, during a real live event.

It is also necessary to evaluate the origin of

requests, in order to detect network branches that

may be overloaded due to an elevated number of

users, or high network utilization by means of

distinct applications like p2p clients or other heavy

consuming software. As has been said, heavy loaded

branches in HFC networks may require the existence

of a proxy that could bring the transmission of

contents closer users. For these cases, it is good

practise to assign specific IP ranges to each of the

network branches, to identify the origin of users’

requests. This policy may also be useful to locate

other transmission problems and solve them with

high efficiency and precision.

Another interesting study is to analyze the

deterioration of the expected quality, understood as

the problems that users are suffering due to an

incorrect selection of audiences –or qualities- during

the production phase. During the configuration of

Figure 4: Evolution of TBR

DEPLOYMENT OF LIVE-VIDEO SERVICES BASED ON STREAMING TECHNOLOGY OVER AN HFC NETWORK

261

production, the most critical step is the selection of

the audiences that will be supported during the

transmission. If this selection is incorrect, customers

may suffer visualization problems due to poor

bandwidth availability. The detection of this kind of

situations can be done using equation 5.

⎪

⎩

⎪

⎨

⎧

−<

≥

EBOB

EQD

Eq. 5

Where EQD is the expected quality deterioration,

OB is the user’s obtained bandwidth, and EB is the

expected bandwidth set during the production phase.

The higher this value is, the poorer the reproduction

quality has been. An elevated number of high values

in this metric should be interpreted as an incorrect

selection of audiences during the production phase

that needs to be reconsidered for future events.

4.3 Visualization Analysis

Visualization analysis has been designed to check

service performance from the users’ point of view.

Therefore, this analysis considers both the quality of

visualization, and the quality of the contents that are

being delivered.

Issues regarding quality of visualization are most

frequently caused by transmission problems, but

users are not aware of the problems that may arise

during the delivery of contents. What users are

aware of is that sometimes the transmission cuts, the

image stops or the initial load time is very high. To

bring this analysis closer to users’ minds or

expectations, all these problems have been grouped

into what can be called Transparency of Service.

Apart from technology evolution, the different

technical solutions or their applicability, the new

services that they offer, etc. every single distributed

service has one goal, and that is Transparency.

When software began to be distributed new

problems arose that had not been considered:

transmission problems, synchronism issues, format

incompatibilities, etc. Live-video, like any other

distributed service, has to assure Transparency.

Users must perceive the reproductions as local to

their computers and have to be unaware of the real

location of the source of the transmission.

Every incidence that takes place in the delivery

of contents, from the production phase to the

visualization of the media in the users’ computers,

has a certain impact on the final reproductions. This

impact is a clear deterioration in the Transparency of

Service. Users’ are aware that there is a problem and

realize that contents are not stored in their

computers. Moreover, they automatically tend to

think that this new –or different- product is worse

than the previous service they already know, e.g.

live Internet video versus conventional TV or video-

on-demand. A metric has been developed to evaluate

this Transparency of Service, using equation 6.

Eq. 6

Where ToS is the Transparency of Service, AQ is

the audio quality, VQ is the video quality, CI is the

coefficient of interruption, ES is the value of the

expected stop metric, WC is the waiting coefficient,

and λ is the coefficient that adjusts the results of the

metric to the preferences of service managers. A

value for λ greater than 1 corresponds to analyses

that give more importance to the quality of

visualization. On the other hand, a value less than 1

gives more importance to the rest of the features.

Audio quality, or AQ, is calculated as the

percentage of requests without lost or delayed audio

packets, and no failed audio resends. Video quality,

or VQ, is obtained equally to AQ, but using video

packets information.

On the other hand, the coefficient of interruption,

or CI, indicates the quality of reproductions from the

point of view of buffer reloads. Whenever a client’s

buffer is consumed, the current reproduction is

stopped until new packets have filled a certain

amount of this buffer. A high percentage of buffer

reloads is symptom of a poor quality in the

reproductions. Thus, this coefficient tries to obtain

the impact of those interruptions by calculating the

percentage of reproductions with no buffer reloads.

The expected stop metric or ES, considers the

fact that, sometimes, the reproductions do not end

for natural reasons, but for transmission problems.

Therefore, it tries to estimate the control level that

users have while viewing the contents, obtaining the

percentage of requests that end with the interaction

STOP, or because the transmission has finished.

The waiting coefficient, or WC, estimates the

effects of the time that users have to wait until their

reproductions start. During this interval, the

communication between the clients and the server is

established, and the client’s buffer is loaded. If these

tasks require too much time, users may feel

disappointed and decide to abandon their requests.

This metric tries to obtain the influence of this effect

by calculating the value of equation 7.

⎪

⎩

⎪

⎨

⎧

≥

load

eRoll

loadeRoll

*100

100

Eq. 7

Where t

PreRoll

is the estimated load time during

the production of contents and t

load

is the real load

time measured in the users’ clients.

(

)

2*3

++++

WCESCIVQAQ

ToS

ICETE 2004 - WIRELESS COMMUNICATION SYSTEMS AND NETWORKS

262

Once the quality of the reproductions has been

checked, it is also very important to ensure that the

offered contents meet the customers’ preferences.

Several metrics have been developed regarding this

issue, the most important being the Impact of the

Service or IoS. It must be taken into account that

while in Web services the only metric that evaluates

the quality of contents is the number of accesses, in

video transmission two different aspects must be

considered: the number of accesses and their length,

the information being continuous. IoS evaluates both

aspects, and checks the quality of the offered

contents using equation 8.

∑

RIUVP

IoS

*100

Eq. 8

Where VP is the visualized percentage, IU is the

interested users metric, and RIU is the really

interested users metric. VP is the amount of

transmission that users have been through. It

compares the duration of the requests with the length

of the full transmission, obtaining the resulting

percentage. It must be taken into account that this

metric is not eligible for continuous broadcasts (like

conventional TV), because there are no time

limitations. Although in continuous transmissions it

could be applied to specific time ranges or

programmes, a value of 100 should be used to

calculate the IoS. IU represents the users that have

been attracted by the access pages or the

advertisements that have been distributed. For its

calculation, the total number of different users shall

be counted in the server or proxies log files. RIU

considers all the users that, apart from being

attracted by the access information, have spent

certain time connected to the service. This time

depends on the provider’s preferences and can range

from a few seconds to several hours.

5 CONCLUSION

The configuration and deployment of live-video

services is an extremely complex process, due to the

high resource consumption of these services, and the

difficulty of transmitting continuous information

over a shared data network. Nowadays, this task is

mainly based on managers’ experience. However, a

formalization of the steps which must be followed to

attain a service of quality, could improve the

obtained results increasing service performance and

profitability. The proposed engineering method and

the expounded approach to service analysis have a

direct applicability in HFC networks and they are

perfectly compatible with other types of networks. It

could also be the base for the development of a

complete analysis and configuration methodology

that could support service management tasks using

production information.

ACKNOWLEDGEMENT

This research has been financed by the network

operator Telecable and the newspaper La Nueva

España within the NuevaMedia, TeleMedia and

ModelMedia projects.

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