Achieving High Interactivity and Stability of a
Videoconferencing System over ATM/Internet
Ting-Chao Hou, Member, Chorng-Horng Yang, and Kim-Joan Chen, Nonmembers
Department of Electrical Engineering
National Chung Cheng University
160, San-Hsing, Ming-Hsiung 621
Chia-Yi, Taiwan, R.O.C.
[email protected], [email protected], [email protected]
Summary
A model of the interactive videoconference is proposed for investigating the interactivity of the videoconference. Through running a prototype videoconferencing system over ATM/Internet networks, we observed that the system stability
would degrade abruptly if the interaction demand from conferees exceeds what
the system control can support. By using the proposed model, we formulate the
problem of achieving high interactivity and stability as maximizing interactivity
by tuning system parameters subject to some stability constraints. Solving the
problem is non-trivial since it involves the unpredictable network delays. We thus
develop practical approaches that can choose and dynamically adjust, according to
the network conditions, the values of the system parameters to meet the stability
constraints and improve the interactivity. Finally, we validate our approaches and
provide guidelines on choosing the parameter values by conducting experiments
and simulations.
key words: interactivity, videoconferencing, system stability, ATM, Internet
_____________________________
This work is supported in part by National Science Council, Taiwan, R.O.C., under Grant NSC-86-2213-E-194-032.
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1. Introduction
Recently, researchers have developed many ATM-based videoconferencing systems by using
various technologies and standards [1]. Through running the systems, they can further
demonstrate multimedia services and investigate the systems’ behavior. Choe et al. employed
the popular middleware technology to implement an ATM-based videoconferencing application [2] and, moreover, showed the flexibility of middleware. Using the real-time computing
technology and ATM technology jointly, Smith and Pretty developed a prototype videoconferencing system [3], which can support broadband distributed communications. A VideoMan
system [4] based on the active network technology demonstrates not only its sophisticated
conferencing services but also high utilization of network resources. Blum et al. presented a
communication platform [5], on which they can quickly implement networked multimedia
applications with conference character. We have developed an ATM-based videoconferencing
(AMV) system [6]-[8] on a multimedia distribution platform [9] in order to explore highly
interactive videoconferences over wide-area networks.
One of the goals of our AMV system is to evaluate the system’s control capability [7]-[8]
of performing highly interactive videoconferences in a WAN environment. To investigate the
control capability, we propose a videoconference model and a performance index, interactivity. A videoconferencing system that has good control capability could react quickly to conferees’ interaction requests, such as join, by quickly rearranging the audio/video distribution,
so that the video freeze time can be made shorter and the interactivity is then higher. However,
improperly using approaches such as the parallel or pipelining approaches to shorten the control time could introduce system state inconsistency, which would degrade the system stability.
In this paper we formulate the problem of achieving high interactivity and stability as maximizing the interactivity by choosing proper values for the system parameters given the con-
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
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straints of system stability. However, solving the problem explicitly is difficult since the
problem involves unpredictable network delays. Thus, we propose approaches that can choose
and dynamically adjust the values of the system parameters according to the network conditions. We also conduct experiments to validate our approaches, and provide guidelines on
choosing the parameter values through running simulations.
The rest of this paper is organized as follows. In Sect. 2, we briefly introduce the service
features, which are required for realizing the interactive videoconferencing services, and the
generic control/management operations, which can be used to accomplish the service features.
In Sect. 3, we describe a prototype of the AMV system that can support the interactive videoconferencing services. We discuss the interactivity and stability of the videoconferencing
system and present the problem formulation in Sect. 4. In Sect. 5, we describe the approaches
of choosing and adjusting the parameter values, and then we discuss the results of the simulations and the experiments. Finally, we conclude this paper in Sect. 6.
2. Interactive Videoconferencing Services
2.1 Service Features
A good videoconference can closely emulate the face-to-face conference. We specify the required service features [6] for the interactive videoconference as follows: 1) Continuous
Presence: Each conferee in a videoconference should be capable of controlling at will the receipt and assimilation of audio/video information. 2) Conference Control: The conference
control defines and realizes the rules of how a conference starts/terminates and how the conferees join/leave the conference. 3) Floor Control: The floor control is responsible for fairly
offering conferees opportunities to speak and well conducting the proceeding of the conference. 4) Status Report: Each conferee naturally has his/her specific status information (e.g.,
state (present or absent), duty (chairperson, speaker, or listener), etc.). In a videoconference,
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
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obtaining the remote conferees’ status information is not trivial so a management feature, status report, is necessary. 5) Failure Recovery: The network congestion and/or failures may
cause the disruption of videoconferencing services. Thus the failure recovery is needed for the
videoconference.
2.2 Generic Control and Management Operations
To facilitate the fast implementation of the service features we proposed the following generic
control
and
management
operations
(CMOPs)
[6].
1)
CAVS:
Create
a
point-to-point/point-to-multipoint Audio/Video Stream. 2) TAVS: Terminate an Audio/Video
Stream. 3) XAS: miX Audio Streams. 4) XVS: patch (miX) Video Streams. 5) MSS: Monitor
the State of a Stream. 6) RFS: Restore a Failed Stream. 7) MSDM: Monitor the State of a multimedia Device Manager. 8) RFDM: Recover a Failed multimedia Device Manager. For example, we could employ CAVS, TAVS, XAS, and XVS to implement the continuous presence.
Moreover, since the conference control and the floor control eventually result in the rearrangement of audio/video distribution, we can use the same CMOPs that achieve the continuous presence to construct the two controls. Obviously, we require the MSS and the MSDM to
accomplish the status report; and we need the RFS and the RFDM to realize the failure recovery. For a detailed discussion of the service features and the CMOPs, see our previous study
[6]-[8].
3. The AMV System
First we briefly describe the AMV system architecture and the implementation. Then we discuss the experiment results, which we observed from field trials.
3.1 System Architecture
Fig. 1 shows the functional blocks of the AMV system. At the bottom of the functional blocks
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
transport service
web-based control/management
Interface
Interface
Audio/Video
Continuous
Presence
Control
Conf-Ctrl.
Floor-Ctrl.
4
Management
S-Report
F-Recovery
Multimedia Application Development Platform
Multimedia
Distribution
Platform
Control and
Management
Platform
ATM Network
Created using UNREGISTERED Top Draw 3/20/100 11:40:34 PM
Fig. 1 Functional blocks.
is the ATM network. The ATM network can support unicast/multicast virtual channels (VCs)
for transmitting high-quality audio and video. Moreover, control and management at the system level can make use of the VCs for distributing (gathering) the commands (responses) to
(from) remote multimedia devices. In addition, ATM networks support routing and signaling,
traffic management, and fault/performance management, which we can use to construct the
CMOPs. On top of the ATM network are multimedia distribution platform (MDP) and control
and management platform (CMP). The MDP can distribute high-quality audio and video by
making use of ATM VCs. The CMP controls and manages the MDP, and exports the CMOPs
to be invoked by the service features. So the ATM network, the MDP, and the CMP constitute
a multimedia application development platform, on which application developers can implement the service features easily and quickly. Above the CMP are the five service features, in
which the continuous presence is the transport service and the other four service features constitute the service control and management. Moreover, two user interfaces are required to
provide conferees with easy ways to select the audio/video and to control/manage the videoconference, respectively.
In the prototype AMV system, we designed and implemented the CMP [6] similar to [5]
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to facilitate the fast implementation of the service features for videoconferencing applications.
Besides the CMP, possible solutions include the popular middleware technology [2], which,
however, does not provide a direct way to improve the system reliability and stability [10]. In
addition, we have developed web-based control and management [7]-[8] for interactive videoconferences. Since a commercial product, SVA system [9], can support the continuous presence, we chose the SVA system as the MDP.
The above control and management functional blocks can be implemented as either a
distributed architecture or a centralized architecture. Software modules in a distributed architecture are running at different computers. Some critical issues, such as coordination of the
distributed software modules and synchronization of the distributed data, have to be carefully
dealt with. On the other hand, the software modules in a centralized architecture are running
at a computer or a control and management server (for short, CM server). So the coordination
and synchronization can be dealt with more easily. Though the risk of the centralized architecture is the crash of the server, to recover the crash of the centralized server is easier than to
recover the failed distributed system. Thus, we selected the centralized architecture [6] for the
prototype of the AMV system.
3.2 Implementation
We briefly introduce the SVA system and then describe the implementations of the CMP and
the web-based control and management.
3.2.1 The SVA System
The SVA system contains two types of hardware devices: Audio Video Adapter (AVA),
which compresses the analog audio/video signals into Motion-JPEG stream, and ATM TV
(ATV), which decompresses the Motion-JPEG stream back to analog audio/video signals. The
software in SVA includes managers (i.e., AVA/ATV managers), trader, and applications (i.e.,
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
D/D-Mgr
user
Conf-Cntrl
new
a
a
d
D-mgr
StateReport
Flor-Cntrl
F-queue
x
process
Operation Manager
b
6
circuit
FailRecover
GCMI
serve
Command
Processor Coordinator
c
shared
s
web
server
memory
s
Monitor
CGI
s
C
Internet
Browser Browser
State
Manager
CM server
... Browser
s
C-Mgr
C
Restorator
Fault Manager
Dispatch
ATM networks
ATM
cntrl/mangt
MDP
D-Mgr D-Mgr
...
D-Mgr
Created using UNREGISTERED Top Draw 3/14/100 9:22:00 PM
Fig. 2 Software modules in the AMV system.
SVC real-time display software (SVC-RTDS) and SVA-patch). AVA/ATV managers can
control AVA's/ATV's operations. Trader provides naming services of managers for applications. The SVC-RTDS application is capable of specifying the parameters (e.g., frame rate) of
an audio/video stream by controlling the source AVA manager. SVA-patch can create and
patch together audio/video streams by controlling source AVA and sink ATV managers. Thus,
the SVA system can distribute high-quality audio/video over ATM networks and patch multiple video streams together at the receiving end. Further details of the SVA system are referred
to [6], [9].
3.2.2 The CM Server
Fig. 2 shows the software modules in the AMV system. There are the control/management
functionalities of the ATM network, the device managers (D-Mgr) of the SVA system, the
software modules in the CM server, and the web-based user interfaces (i.e., browsers and a
web server). We implemented the CMP and the four service features for control and management in the CM server. Note that, in the rest of the paper, feature functions refer to the implementations of the service features. Besides, we developed web-based user interfaces,
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
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which cooperate with the four feature functions in the CM server to achieve web-based control and management. The CM server contains four main modules: operation manager, fault
manager, dispatch, and CGI. The four modules communicate with each other through a shared
memory and use semaphores to control the access to the shared memory. Several tables and a
floor queue are designed for the operation manager and the fault manager. We briefly describe
how the CM server works as follows.
The generic control and management interface (GCMI) in the operation manager receives the CMOP invocation from the feature function and then checks the arguments of the
CMOP invocation. If the check passes, GCMI sends the CMOP invocation to the coordinator,
which then converts the CMOP into the corresponding subprocedures. Each subprocedure is a
control flow, a management flow, or a signaling flow. A control flow activates the command
processor to control the MDP; a management flow activates the state manager to manage
MDP and/or access ATM management functionality. A signaling flow activates the channel
manager (C-Mgr) to setup ATM channels. The command processor can generate the corresponding control messages (i.e., SVA instructions) for the control flow. These control messages are delivered via ATM VCs to the software in the SVA system. Then, the software in
the SVA system receives and executes the control messages, and thus the control is done.
Similarly, the state manager can send device managers the SVA instructions to get the states
of the device managers. Moreover, the state manager can activate the monitor, which uses
ATM F4/F5 flows to achieve the fault/performance management of the VPs/VCs, to obtain
the state of a VC. The C-Mgr, besides setting up VCs for both the command processor and the
state manager, feeds (collects) instructions (responses) to (from) the channels. Note that we do
not implement the restorater in the current prototype of the AMV system.
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
(a) The web-based user interface.
8
(b) A snapshot of the AMV system.
Fig. 3 The prototype AMV system.
3.2.3 Web-Based User Interfaces
We developed the web-based user interfaces that can provide all conferees with an easy way to
control and manage the videoconference. Conferees can initiate interaction requests (e.g., join)
at their own browsers (see Fig. 2). Each request is sent to the web server and then to the CGI
through Internet. CGI then activates the corresponding feature functions (e.g., conference control) in the operation manager and then sends results obtained from feature functions to conferees. We show the web-based user interface and a snapshot of operating the AMV system in
Fig. 3. For more details of the interface and the implementation, see [6].
3.3 Field Trials
Fig. 4 shows the ATM LAN/WAN environments for the field trials. The system, when ran in
the WAN environment, supported nine conferees to participate in the videoconference. Multicast of audio and video information ran smoothly and the feature functions provided the expected conference/floor controls and status report services. However, we also observed the
following less satisfactory experiment results, which we denote as ER1 and ER2.
ER1) Conferees whose floor requests traverse longer routes from their browsers to the web
server often fail in floor competitions. Consequently, the conferees who are impatient
may generate lots of repetitive interaction requests, which make the web server over-
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
NCU
NCTU
9
NTHU
NCKU
Chia-yi
Taipei
NTU
NSYSU
Kaoshuing
Taiwan experimental
ATM network
NCCU
cc
Fore
ASX-200
Nat'l C.C. Univ.
campus ATM netw ork
Fore
ASX-200
ATV
NCCU
ee1
NCCU
ee2
PC
AVA
Web-Server C/M-Server
Fig. 4 Field trials in the ATM LAN and WAN.
loaded.
ER2) When conferees interact highly with one another, the CM server continuously executes
lots of CMOPs to accomplish these bursty interactions. To execute these CMOPs, the
CM server dispatches control messages to remote device managers through the ATM
network. Unfortunately, network congestion imposes longer delays on the control messages that go through the congested region and therefore disturbs the timing relationship
among the control messages that are dispatched to different device managers. Most of
the disturbances cause late responses from the system, but the fatal one, which violates
the order of completing the CMOPs, can cause system state inconsistency that makes
the system uncontrollable and unstable.
The experiment results show that if conferees’ demand for conference interactions exceeds what the system control can support, the system stability degrades abruptly. In the rest
of the paper, we investigate the interactivity and the stability of the AMV system.
4. Interactivity and Stability
4.1 Videoconference Model
The following notations are used in the rest of the paper.
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
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IVS
i0
begin
fi
fi-1
c0 s0
... Q
Ii-1
join, leave, etc.
floor-rqts
Pi-1
i-1
Ci-1
Si-1
Ii
Qi
Pi
end
...
Si
Ci
in+1
ra,k
rb,k'
M={a,b,c}
dummy

rc,k''
i
cmop1
...
i
...
cn+1
cmop2
i
...
...
cmopk
i
di
Created using UNREGISTERED Top Draw 3/14/100 10:40:23 PM
Fig. 5 Videoconference model.
IVS : interactive videoconferencing service.
I i : i -th interaction of the IVS .
Q i : interval in which the CM server collects interaction requests in I i .
 : interval in which the system is open up for conferees to initiate interaction requests.
ra ,k : k -th interaction request that conferee a initiates in IVS .
Pi : interval in which the CM server processes interaction requests in I i .
C i : interval in which the CM server controls the system in I i .
S i : interval in which the system provides conferees with audio/video services.
f i : video freeze time in I i .
s i : service provision time in I i .
T f : predefined floor holding time.
M : group of conferees.
xi : interactivity of I i .
X : interactivity of the IVS .
As shown in Fig. 5, the IVS is a series of interactions enclosed with the begin and the
end operations: i0 , I1 , , I n , in1 , where i 0 and in 1 denote the begin and the end opera-
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
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tions, respectively. The interval between the start and the termination of an interaction consists of four parts: Qi , Pi , C i , and S i . During Qi , the CM server collects interaction requests from the web-based user interfaces. Due to the asynchronism of the web-based user
interfaces, the AMV system pre-allocates an interval  after S i 1 for conferees to initiate
interaction requests in I i . The system then determines a proper Qi so that the CM server
will receive the interaction requests issued during  interval with high probability. If the
CM server receives an interaction request after Qi , it will defer processing the request to the
next interaction. On the other hand, if the conferee does not initiate any interaction request in
 , the conferee’s browser automatically sends a dummy interaction request at the end of 
to report the browser’s state to the web server. During Pi , the CM server processes the interaction requests that it receives in Qi and, after the processing, generates a control scenario,
which specifies the CMOP invocations that are required to accomplish the interaction. Next,
during C i , the CM server executes the CMOPs according to the control scenario. After the
CM server executes the CMOPs, the service is available during S i .
In this paper, we assume that the video is frozen during Qi , Pi , and C i . The audio/video information during these three intervals is set to the residue of the audio/video information of the previous interaction, I i 1 . Thus the video freeze time f i is the sum of the
lengths of the three intervals, i.e., f i  t L (Qi )  t L ( Pi )  t L (Ci ) , where we use t L () to denote the length of an interval. Moreover, we assume that for each interaction the floor control
always results in the floor transfer (i.e., the rearrangement of the audio/video distribution) and
the speaker completely uses up the predefined floor holding time in addressing. Thus, the service provision time s i , which is defined as the length of S i , is assumed to be the constant
T f (i.e., si  t L ( S i )  T f , i ).
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
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4.2 Problem Formulation
4.2.1 Interactivity
Definition 1: The interactivity of I i is defined as the ratio of s i to f i  si , i.e.,
xi 
si
.
f i  si
Definition 2: The interactivity of IVS is the average over all xi , i  1,  , n , i.e.,
X 
1 n
 xi .
n i 1
Ideally, a videoconferencing system that has zero video freeze time for every interaction
could achieve the maximum interactivity of IVS , which is equal to 1. In reality, the value of
the video freeze time f i may be up to several seconds. To maximize the interactivity of
IVS , we have to maximize the interactivity of each interaction, xi , i  1,  , n . (In the rest of
this paper, interactivity refers to xi only.) Furthermore, since s i is set to be a constant T f ,
maximizing the interactivity xi is equivalent to minimizing the video freeze time f i or, in
other words, minimizing each of the three components of f i .
Intuitively, to minimize the three components, we shall choose a small value for t L (Qi ) ,
run the CM server in a computing platform with excellent processing capability to reduce
t L ( Pi ) , and shorten t L (Ci ) by adopting parallel or pipelining approach in controlling the
system. However, improperly choosing a small value for t L (Qi ) to obtain high interactivity
can cause ER1, which degrades the system stability. Similarly, issuing control messages in a
parallel or pipelining way may introduce system state inconsistency, which further makes the
system uncontrollable and unstable, as described in ER2. To further explore the problem, we
derive the stability constraints within which we can maximize the interactivity without de-
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
13
grading the stability. Note that, we do not discuss how to minimize t L ( Pi ) since running the
CM server in a computing platform with good processing capability is a feasible solution.
4.2.2 Stability Constraints
To solve the problem in ER1 is to choose a proper value for t L (Qi ) so that the interaction
requests from different conferees can reach the web server without the distance bias. Thus, we
have a constraint on the value of t L (Qi ) . Let t as ,k denote the time that conferee a initiates
interaction request ra ,k at conferee a ’s browser and t ar ,k denote the time that the web server receives ra ,k .
Definition 3: Eligibility of Interaction Request
An interaction request ra ,k is eligible for Qi if and only if a  M and ra ,k is initiated in  of Qi . We denote the eligibility of ra ,k by ra ,k  Qi .
Definition 4: Constraint of Web-Based Interface Responsiveness (C1)
For any interaction request ra ,k  Qi , the system must meet the following constraint so
that the system can respond to ra ,k .
t L (Qi )  t L ( )  t ar,k  t as,k
,
(a, k )
satisfying
ra ,k  Qi
.
(1)
Next, to formulate the problem described in ER2, we first discuss the control approach
that the CM sever adopts as follows. During C i , the CM server executes several CMOPs to
accomplish the interaction. Executing a CMOP involves creating control flows, which is
mapped into dispatching control messages through ATM VCs to remote device managers.
Previous studies [11]-[12] in the ATM traffic and congestion control shows that the conges-
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
14
tion caused by highly bursty traffic (e.g., videoconferencing) arises and dissipates quickly.
This can explain why the time for executing a CMOP can vary greatly. If the CM server executes these CMOPs sequentially (or, in protocol vocabulary, stop-and-wait), the value of
t L (Ci ) will be large and the interactivity will be low. Thus we adopted a pipelining approach
for executing CMOPs. As shown in Fig. 5, we insert a time gap  i between executing two
adjacent CMOPs in C i . Suppose that the number of CMOPs that the CM server executes in
C i is N i . Let t Ce i , j denote the time that the CM server starts executing the j -th CMOP in
C i , and t Cf i , j denote the time that the CM server finishes the execution of the j -th CMOP
in C i . By definition,
 i  t Ce , j  t Ce , j 1 , j  N i ,,2 .
i
i
(2)
Then we can calculate the value of t L (Ci ) as follows.
t L (Ci )  ( N i  1) i  tCfi , Ni  tCe i , Ni .
(3)
To make the system controllable requires maintaining the system states consistency or, in
other words, maintaining the correct ordering of completing CMOPs. So, we have the following constraint.
Definition 5: Constraint of System State Consistency (C2)
For each CMOP that the CM server executes in C i , the system is stable if the system
meets the following constraint.
tCfi , j  tCfi , j 1 , j  N i ,,2 .
We add Eq. (2) to Eq. (4) and then rewrite Eq (4) to obtain
(4)
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
 i  (tCf , j 1  tCe , j 1 )  (tCf , j  tCe , j ) , j  N i ,,2 .
i
i
i
i
15
(5)
4.2.3 Optimization Problems
We formulate the problems as minimizing t L (Qi ) and t L (Ci ) subject to constraints C1 and
C2, respectively. We denote the optimization problems as P1 and P2, respectively.
(P1) minimize t L (Qi ) ,
subject to t L (Qi )  t L ( )  t ar,k  t as,k , (a, k ) satisfying ra ,k  Qi .
(P2) minimize t L (Ci )  ( N i  1) i  t Cfi , Ni  tCe i , Ni ,
subject to  i  (t Cf i , j 1  t Ce i , j 1 )  (t Cfi , j  t Ce i , j ) , j  N i ,,2 .
5. Approaches for Choosing and Adjusting Parameters Values
5.1 Time needed to Collect Interaction Requests
To solve P1 is to choose the minimal value for t L (Qi ) that can meet constraint C1. Since
t L ( ) is a constant, minimizing t L (Qi ) is equivalent to minimizing  i  t L (Qi )  t L ( ) .
Moreover, we denote the maximum of the one-way delays in transmitting the eligible interaction requests from the browsers to the CM server as
dimax 
max
 ( a , k ) s .t . ra ,k Qi
(tar , k  tas, k ) .
(6)
Then, we can rewrite C1 as
 i  d imax .
(7)
So for our discussion, we can describe P1 as follows: after the interval of  in each interaction I i , the CM server should create a window of length  i for collecting all interaction
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
16
requests that are eligible for Qi . To obtain the minimal value for  i is non-trivial since the
value of d imax is time-varying and dependent on the unpredictable network delays. Thus, we
employ the following approach that can dynamically adjust the value of  i for each interaction. The approach takes into consideration the fluctuation of the Internet delay and also
whether a conferee is participating the videoconference.
5.1.1 Approach
To choose a proper value for  i requires measuring one-way delays from the conferees’
browsers to the web server. However, measuring the one-way delays is more involved in that
it needs running the network time protocol (NTP) in the computing platforms that the browsers and the web server reside in. Thus, instead of the one-way delays, we use the
round-trip-time (RTT) delays on determining the value of  i . Moreover, there is a timing
requirement that the CM server sets the value of  i before creating the window  i . So we
develop our approach as follows.
During S i 1 the web server regularly sends pseudo interaction requests to each of the
conferees’ browsers to measure the RTT delays. Each pseudo interaction request is encapsulated in an UDP packet. Let sra ,k denote the k -th pseudo interaction request that the web
server sends to conferee a ’s browser, where a  M , and successfully receives the corresponding acknowledgement. The maximum RTT delay during S i 1 , max_rtti 1 , is updated as
the web server received an acknowledgement of sra ,k during S i 1 . Our approach chooses
the value of  i as
 i  T  max_rtti 1 ,
(8)
where T is a multiplicative factor. (Note that the begin operation, i 0 , (see Fig. 5) includes
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
17
the interval S 0 for the system to obtain 1 .) The value of T is chosen according to the environment in which the AMV system runs. For example, the value of T can be set to 0.8
when the system runs in a LAN environment. On the other hand, when we run the system in a
WAN environment, we can set the value of T to a larger value, e.g., 1.6. The web server
continuously updates the maximum RTT delay during S i 1 and passes it to the CM server
during  of Qi .
5.1.2 Experiments
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
18
The first experiment (Dec. 14, 1999)
1
T=1
T=1.5
0.9
0.8
0.7
m
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5
10
15
20
25
30
35
sequence number of interaction
40
45
50
(a) The ratio of d i to i .
The first experiment (Dec. 14, 1999)
3500
T=1
T=1.5
3000
w (ms)
2500
2000
1500
1000
500
0
0
5
10
15
20
25
30
35
sequence number of interaction
40
45
50
(b) The value of i .
Fig. 6 Results of the first experiment.
We conducted two experiments to validate our approach. In the experiments, six conferees at
three universities (i.e., NCCU, NCTU, and NTUST) join the videoconference, which consists
of 50 interactions. The value of T f is set to ninety seconds and the value of  is set to five
seconds. To evaluate the approach we define an index mi as follows. The web server monitors the arrival times of the interaction requests that are eligible for Qi . (Note that each of the
browsers sends at least one eligible interaction request, which is either the conferee’s interac-
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
19
tion request or the dummy one that the browser automatically generated.) After the web server
receives all eligible interaction requests, the web server calculates the time difference between
the arrival time of the last interaction request and the time at the end of  . We denote this
time difference as d i (see Fig. 5). Using  i and d i , we define the index mi as
mi 
d i
i
.
(9)
Obviously, the optimal value of mi is one, which means that the window is just large enough
for collecting all eligible interaction requests. If the value of mi is greater than one, the
window is too small so that the constraint C1 is violated. On the other hand, if the value of
mi is less than one, the CM server creates a larger window that reduces the interactivity. Fig.
6 and Fig. 7 show the results of the experiments. As shown in Fig. 6-(a), the value of mi is
less than one for each interaction or, in other words, C1 is not violated during the videoconference. The average value of mi is 0.5081 (0.3387) when we set the value of T to 1 (1.5).
Fig. 6-(b) shows the value of i for each interaction. Note that a spark [13] of the Internet
delay occurs in S 24 so that the value of  25 is large. The value of m 25 is small because the
suddenly jump in Internet delay goes away in Q25 . In the second experiment, C1 is violated
only at the sixth interaction (see Fig. 7-(a)) since the Internet delay increases suddenly in Q6 .
This delay increase was not present at S 5 . The average value of mi is 0.5209 (0.3473) when
we set the value of T to 1 (1.5). Fig. 7-(b) shows the value of i for the second experiment.
Note that the value of  7 is large since the Internet delay increases in not only Q6 but also
S 6 . The experiment results show that our approach can dynamically adjust the value of  i
according to the network conditions though the spark of the Internet delay, which is unpredictable and rare, may violate C1 or reduce the interactivity.
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
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The second experiment (Dec. 17, 1999)
2.5
T=1
T=1.5
2
m
1.5
1
0.5
0
0
5
10
15
20
25
30
35
sequence number of interaction
40
45
50
(a) The ratio of d i to i .
The second experiment (Dec. 17, 1999)
1500
T=1
T=1.5
w (ms)
1000
500
0
0
5
10
15
20
25
30
35
sequence number of interaction
40
45
50
(b) The value of i .
Fig. 7 Results of the second experiment.
5.2 Time Gap for Pipelining Approach
To minimize the control time t L (Ci ) , we would minimize N i ,  i , and (t Cf i , Ni  t Ce i , Ni ) . N i
depends on the type of interactions and the number of conferees who issue interaction requests. It is not under the control of the system designer. The value of (t Cf i , Ni  t Ce i , Ni ) is unpredictable since executing a CMOP involves dispatching control messages through the ATM
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
21
network to device managers and executing the control message by the device manager at the
multimedia device. Thus, to solve P2 is to choose the minimum value for  i to meet C2.
However, choosing a proper value for  i is also non-trivial since the execution time of a
CMOP varies greatly and depends on both the ATM network conditions and the remote device managers that are involved in the CMOP. To provide guidelines on choosing the value of
 i , we developed simulation studies that help us choose a proper value for  i .
5.2.1 Simulation Studies
Before discussing the simulation studies, we define a performance index, degree of stability
degradation (SD), for an interaction as follows.
Definition 6: Degree of SD, dsd , is the total number of CMOPs that violate constraint C2 in an interaction.
A system is stable if its dsd for each interaction is zero, but a system that has one or higher
dsd for some interactions would suffer from system state inconsistency or, in other words,
lower system stability.
• Simulation environments: We focus on three types of interactions: join, floor transfer, and
leave. To accomplish the three interactions, the CM server executes the corresponding
CMOPs. For example, the floor transfer involves TAVS, CAVS, MSDM, MSS, and XVS. Each
of the CMOPs is characterized as either single point control or multipoint control. The execution time of a CMOP consists of two parts: a constant minimum execution time B and
a fluctuation part, which is generated using the exponential random variable with mean  .
Note that B represents the sum of the minimum transmission time that the CM server
dispatches the control messages to device managers and the minimum execution time that
the device manager executes the control messages. The fluctuation part represents the delay
fluctuation in dispatching the control messages from the CM server to device managers.
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
22
Table 1 Categories of CMOPs.
CMOPs
B

SP
TAVS, MSDM, CAVS,XVS
50 ms
20 ms
MP
CAVS, MSS
50 ms
40 ms
SP
TAVS, MSDM, CAVS,XVS
0.1 s
0.1 s
MP
CAVS, MSS
0.5 s
0.6 s
Categories
LAN
WAN
1. SP: single point, MP: multipoint, 2. CAVS for floor transfer, CAVS for join.
Table 1 shows the categories of the CMOPs and the parameter values. We define a series of
conferee interactions as a benchmark to evaluate the system stability. The benchmark is as
follows. i) Five conferees join the videoconference one by one. ii) All conferees, except the
conferee who just releases the floor, compete for the floor for twenty interactions. iii) Conferees then leave the videoconference one by one. We can then figure out how many
CMOPs are required to fulfill each interaction defined in the benchmark.
• Simulation experiments: To simplify the problem, we let  1   2     n   . We conducted simulation experiments in both the LAN and the WAN environments. And we discuss only the simulation experiments in the WAN environment as follows. The outcomes of
the experiments are i) the average control time for the three interactions versus the  value
and ii) the probabilities of finishing the interactions without violating C2 (i.e., zero dsd )
versus the  value. Fig. 8-(a) shows that the average control time of each interaction
grows linearly as the  value increases. For example, if the  value is set to 1000 ms, the
average control time of the floor transfer is 4.21 seconds. We denote the average control
time of the floor control as C f   .
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
6
14
5
12
23
ep=0.95
ep=0.90
floor transfer
join
leave
average control
Nr time (s)
10
4
8
3
6
2
4
1
2
0
0
1000
0
1500
500
1000
2000
2500
1500
 (ms) 2000
delat  (ms)
2500
3000
3000
3500
3500
(a) control
N r . time.
(a) Average
221
ep=0.95
ep=0.90
0.9
20
Probability
zero dsd
Expected of
control
time (s)
0.8
18
0.7
16
0.6
0.5
14
0.4
12
0.3
¬ Min ctrl time(ep=0.95)
10
0.2
¬ Min ctrl time(ep=0.90)
8
0.1
1000
0
0
1500
500
1000
2000
2500
1500 (ms) 2000
delta  (ms)
leave
join
floor transfer
3000
3500
2500
3000
3500
(b) Expected control time.
(b) Probability of zero dsd .
Fig. 9 Tuning the  value.
Fig. 8 Results of simulation experiments.
Fig. 8-(b) shows that the probability of zero dsd for each of the three interactions
increases as the  value increases. For example, the probability of zero dsd , if the 
value is set to 1000 ms, is 0.465 for floor transfer. We denote the zero dsd probability
function for floor transfer as Pf   .
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
24
5.2.2 Choosing a proper value for 
The floor transfer, which is the most complex among the three interactions, requires the longest control time and has the lowest probability of zero dsd . So we tune the  value by considering the floor transfer. Let ep be the percentile probability that the AMV system can
successfully finish a floor transfer interaction. Let N r  , ep be the number of attempts to
fulfill the floor transfer so that such attempts will result in ep percentage of success in finishing the floor transfer given Pf   . So,
ep 
N r  
 1  P  
i 1
i 1
f
 Pf   , where N r   N r  , ep .
(10)
For example, we may choose two percentiles, ep1  0.90 and ep2  0.95 . Then, given the
zero dsd probability function Pf   and the percentile ep , we can obtain N r  , ep by
solving Eq. (10).
Fig. 9-(a) shows two stepwise curves that are N r  , ep  0.90 and N r  , ep  0.95 ,
respectively. For example, given   1000 ms and ep  0.95 , we obtain Nr  5 . It means
that the AMV system, on the average, may need to execute five floor transfer interactions to
have a 95 percent probability of not violating constraint C2. Then, we can obtain the expected
control time, C ( , ep ) , as follows.
C  , ep   C f    N r  , ep 
(11)
The result of C , ep  is shown in Fig. 9-(b). We show two curves for ep =0.90 and 0.95,
respectively. Using the curves, we could find the minimum control time versus the probability
of completing a floor transfer in one attempt, i.e., N r  1. For example, given ep  0.95 , we
obtain that the minimum control time is 10.18 seconds when the  value is set to 2.55 seconds. Similarly, for the case of ep =0.90, the minimum control time is 8.65 seconds when the
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
25
 value is set to 2.15 seconds. Thus, tuning the  value is a trade-off process between
achieving higher interactivity (i.e., minimum control time) and maintaining a stable AMV
system (i.e., zero dsd ). Note that more accurate parameter values can be obtained from field
trials. Thus, our simulation studies provide guidelines on choosing the value of  .
6. Conclusion
We have presented the design, implementation, and experimental results of an ATM-based
videoconferencing (AMV) system. The AMV system can provide conferees with the continuous presence and the web-based control and management for achieving interactive videoconferences in a WAN environment. Through running the AMV system, we observed that
the system stability degrades abruptly if the interaction demand from conferees exceeds what
the system control can support. To study the interactivity and the stability, we proposed a
videoconference model that facilitates the diagnosis of the stability problem. The model also
allows us to formulate the problem of finding approaches to achieve high interactivity but
without sacrificing the stability. Specifically, to achieve high interactivity requires shortening
the video freeze time or, in other words, shortening the time of collecting interaction requests
(i.e., t L (Qi ) ) and the time of controlling the system (i.e., t L (Ci ) ). We derived the stability
constraints and formulated the problem as minimizing t L (Qi ) and t L (Ci ) by tuning the
system parameters subject to the stability constraints. We developed practical approaches for
tuning/adjusting the values of the system parameters according to the network conditions to
improve the interactivity and meet the stability constraints. Finally, we validated our approach
by conducting experiments and we also conducted simulation studies to provide guidelines on
choosing the proper value for the system parameter.
References
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Biographies
Ting-Chao Hou received the B.S. degree in electrical engineering in 1979 from the Nation-
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
27
al Taiwan University, and M.S. and Ph.D. degrees in electrical engineering from the University of Southern California in 1982 and 1985, respectively. From 1985 to 1993, he was a
member of Technical Staff, AT&T Bell Laboratories. Since 1993 he has been with the Department of Electrical Engineering at the National Chung Cheng University. Currently he is
an associate professor. His research interests include ATM switching systems, intelligent
networks, communication network performance analysis, and wireless networks.
Chorng-Horng Yang received the B.S. degree in electrical engineering in 1992 from the
National Taiwan University of Science and Technology, and M.S. degree in electrical engineering from the National Chung Cheng University, Taiwan, in 1994. He is now working towards his Ph.D. degree also in electrical engineering at the National Chung Cheng University.
His research interests include multimedia applications, system management, and group communication protocols.
Kim-Joan Chen received the B.S. degree in mathematics from the National Central University, Taiwan in 1977, and M.S. and Ph.D. degrees in applied mathematics from the State University of New York at Stony Brook in 1981 and 1983, respectively. He then joined the University of Cincinnati, Cincinnati, Ohio, where he was an assistant professor at the Department
of Mathematical Science. During a sabbatical leave from 1984 and 1985, he was with
UNINET/TELENET research and development company where he engaged in data networking research dealing with topology design, performance analysis, and pre-ISDN fiber optical
voice and data networks integration. From 1986 to 1989, he was with AT&T Bell Laboratories. He was responsible for ISDN packet mode performance, dealing with routing, flow control, and congestion control. Since 1989 he has been with the Department of Electrical Engineering at the National Chung Cheng University. He served as the Chairperson of the department from 1989 to 1991 and as the Director of Computer Center from 1994 to 1998. His research interests include control and management of computer and telecommunication networks, video-on-demand, and group communications.
Captions
Fig. 1 Functional blocks.
Fig. 2 Software modules in the AMV system.
Fig. 3 The prototype AMV system.
Fig. 4 Field trials in the ATM LAN and WAN.
Fig. 5 Videoconference model.
HOU et al: INTERACTIVITY AND STABILITY OF A VIDEOCONFERENCING SYSTEM
Fig. 6 Results of the first experiment.
Fig. 7 Results of the second experiment.
Table 1 Categories of CMOPs.
Fig. 8 Results of simulation experiments.
Fig. 9 Tuning the  parameter.
28