VIDEO

TRANSMISSION WITH ADAPTIVE QUALITY BASED ON

NETWORK FEEDBACK FOR MOBILE ROBOT TELEOPERATION

IN WIRELESS MULTI-HOP NETWORKS

Florian Zeiger, Markus Sauer and Klaus Schilling

University of Wuerzburg, Department of Robotics and Telematics, Am Hubland, Wuerzburg, Germany

Keywords:

Networked robots, mobile robot teleoperation, mobile robot ad-hoc networks, wireless network, ad-hoc net-

work, wireless teleoperation, wireless remote control.

Abstract:

A video stream is still one of the most important data sources for the user while remote-operating a mobile

robot. Human operators have comprehensive capabilities to interpret the displayed image information, but

therefore, some constraints must be fulﬁlled. Constant frame rates and delays below a certain threshold are a

minimum requirement to use video for teleoperation. Modern multi-hop networks often use WLAN to set up

ad-hoc networks of mobile nodes with each node acting as trafﬁc source, sink, or router. Considering these

networks, routes between sources and destinations might be established via several relay nodes. Thus, the

utilization of intermediate nodes which are part of a route inﬂuences the overall route performance, whereas

sender and receiver have no direct feedback of the overall route status. In case video is transmitted via wireless

ad-hoc networks in a teleoperation scenario, the displayed video-stream for the operator might have variable

frame rates, very high packet loss, and packet inter-arrival times which are not appropriate for mobile robot

teleoperation. This work presents an approach using a feedback generated by the network to adapt the image

quality to present communication constraints. Thus, according to the current network status, the best possible

video image is provided to the operator while keeping constant frame rates and low packet loss.

1 INTRODUCTION

In the meantime, mobile robots are planned to be

used or even already used in many civil applica-

tions like surveillance or search and rescue to sup-

port and relieve the humans in place. Often, wire-

less communication is chosen to distribute and share

information between the humans and robots in the

team. This includes the transmissions of sensor

data from the robots, observations from the humans,

commands, and plans to the different team entities

from the human coordinators. In many cases IEEE

802.11 wireless LAN is used as underlying tech-

nology for the wireless network interconnecting the

team. Nowadays modern telecommunication equip-

ment with small power consumption and interfaces

for easy integration is available. This even allows an

affordable system of wireless ad-hoc networks of mo-

bile robots and human team members. These wire-

less ad-hoc networks offer a lot of advantages in con-

trast to static wireless network conﬁgurations, but

also raise a lot of new challenges in the system de-

sign. In (Hu and Johnson, 2002) a live audio and

video data transmission via a multi-hop wireless net-

work is demonstrated. In addition, several systems

of rovers with autonomous functionalities (Parker,

1994), groups of unmanned aerial vehicles (Ollero

et al., 2004), as well as heterogeneous multi robot

systems were proposed. Rooker and Birk presented

multi-robot exploration with robots using wireless

networks (Rooker and Birk, 2007). For ground based

systems Chung (Chung et al., 2002) presented a test

bed for a network of mobile robots. In the ﬁeld of

rescue robotics (Rooker and Birk, 2005) or for inte-

grating UAVs into IP based ground networks (Zeiger

et al., 2007) the use of wireless networks is quite

common nowadays. With respect to unmanned aerial

vehicles (UAVs), (Ollero et al., 2003) presented a

system using an access point running in WLAN in-

frastructure mode onboard the UAV. (Vidal et al.,

2002) presented a system for communication between

a ground station and a UAV using WLAN in com-

bination with a high-gain antenna and radio modem.

The University of Pennsylvania presented a mobile

robot team connected via wireless network which

performed localization and control tasks (Das et al.,

Zeiger F., Sauer M. and Schilling K. (2008).

VIDEO TRANSMISSION WITH ADAPTIVE QUALITY BASED ON NETWORK FEEDBACK FOR MOBILE ROBOT TELEOPERATION IN WIRELESS

MULTI-HOP NETWORKS.

In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - RA, pages 69-76

DOI: 10.5220/0001492100690076

 SciTePress

2002). Currently, wireless ad-hoc networks for mo-

bile robots are a challenging and interesting scientiﬁc

topic and scenarios connecting several mobile robots,

humans in place (e.g. search and rescue applications),

and stationary network nodes (e.g. communication re-

lay nodes) are evaluated and analyzed (cf. Figure 1).

Figure 1: Future scenario of a heterogeneous network of

mobile robots and human personnel.

Special ad-hoc routing protocols like AODV (Das

et al., 2003)(Chakeres and Belding-Royer, 2004),

DSR (Johnson and Maltz, 1996), or OLSR (Clausen,

2003) allow communication also in a highly dynamic

network topology which increases the capabilities and

the ease of use of mobile robots. These networks

allow any-to-any communication between all nodes

inside the network on a logical layer. Nevertheless,

the radio link always implies the potential danger of

a complete communication drop-out and the unpre-

dictable loss of packets with a variable packet loss

probability. Also the delay of packets delivered via

the same route by hop-by-hop fashion can be variable.

The same also applies for the bandwidth – e.g. IEEE

802.11 WLAN usually reduces its bandwidth as the

link quality decreases.

The dynamic characteristics of WLAN and es-

pecially if it is used together with ad-hoc routing

protocols has special drawbacks if direct teleopera-

tion should be implemented over these communica-

tion links. Although there was a lot of progress in the

area of autonomy for mobile robots, still many appli-

cations need the direct teleoperation of mobile robots,

which requires in many cases reliable and high band-

width links for video streams from the robots. For

low-bandwidth conditions and very deﬁned environ-

ments, e.g. in tele-education, also virtual representa-

tions can be used to provide the necessary information

for direct teleoperation (Sauer et al., 2005). For the

more dynamic scenarios, (e.g. in search and rescue)

where the application of wireless ad-hoc networks is

very desirable, direct teleoperation with high band-

widths is mostly required. These needs for high band-

width result from the fact, that the video feedback still

delivers the most and richest information from the re-

mote environment to the operator. This detailed infor-

mation from the remote site is needed to increase and

maintain the situation awareness and common ground

between robot and human operator as basis for any

future decisions and commands done by the human

operator. (Murphy and Burke, 2005) showed that this

situation awareness is even more important than any

autonomy or assistance function implemented in the

robot. Dependent on the human teleoperation task

different characteristics of the video stream are im-

portant. If a navigation task is considered, the most

important parameters are a high frame rate, low num-

ber of frame losses, and a constant inter-arrival time

between to frames. Compared to these parameters the

quality and resolution of the video stream is less im-

portant for navigation. On the other hand if the human

has a search task (e.g. identify objects in a delivered

video stream), the quality and resolution has a higher

importance than the frame rate. Here, the proposed

mechanism for the video-stream adaptation according

to the load status of the route is designed for navigat-

ing a mobile robot with direct teleoperation.

The presented mechanism allows a variable image

quality of the video stream for the operator. The qual-

ity is adjusted automatically to the current state of the

wireless multi-hop network and respectively the avail-

able bandwidth of the used route by using a feedback

of the network status. As above mentioned, the state

of each single node of a route has a strong inﬂuence

on the quality of the used link in terms of bandwidth,

delay, and packet loss. To increase the performance

of mobile robot teleoperation, the available frame rate

at the operator PC should be almost constant. In or-

der to adjust the image quality according to the link,

an active feedback mechanism is implemented at the

application layer of each node. Thus, a feedback of

the network is available for the video stream source

which can be used to adapt the image quality. The

proposed mechanism requires only little resources, is

portable and easy to implement, and provides the op-

erator the highest possible video quality for mobile

robot teleoperation which can be guaranteed for the

current network state. As it supports no trafﬁc classes

as it is known from wired IP networks, it should not be

considered as a quality of service (QoS) mechanism.

Anyway, available quality of service (QoS) mecha-

nisms – e.g. integrated services (IntServ) or differen-

tiated services (DiffServ) – are currently not applica-

ble in ad-hoc networks of mobile robots due to very

speciﬁc hardware requirements and the special solu-

ICINCO 2008 - International Conference on Informatics in Control, Automation and Robotics

tions which are currently available for network ser-

vice providers.

The remainder of this work is structured as fol-

lows. In Section 2, the investigated scenario is de-

scribed and a short deﬁnition of the problem is given.

Section 4 presents the implementation of the network

feedback mechanism and the adaptation of the video

stream in detail. The next Section gives an overview

of the mobile robot, the communication, and video

hardware which is used in the test scenarios. Section

5 deﬁnes the test scenarios of real hardware tests and

gives the results. Finally, a conclusion is given in Sec-

tion 6.

2 PROBLEM DEFINITION AND

SCENARIO

2.1 Problem Deﬁnition

In the above mentioned scenarios, the available

throughput of a route via a wireless multi-hop net-

work is a highly dynamic parameter which depends

on many environmental inﬂuences and affects the

quality of the application signiﬁcantly. The through-

put of a wireless node can be decreased due to dif-

ferent reasons. In case intermediate nodes of a route

are also part of a route which has to transport other

bandwidth intensive ﬂows, the available bandwidth

must be shared between all present routes via this

node, which will reduce the available bandwidth for

the video link. Furthermore, also a decreasing link

quality will reduce the bandwidth and increase the

packet loss probability. If the network is not react-

ing to trafﬁc overload at a speciﬁc node, this will lead

to unpredictable packet loss at this point and delays at

the different receivers. For the teleoperation scenario

the effect will be that the video stream will get ran-

domly stuck, because packets get lost. Most probably

the operator will get confused and will stop the robot.

2.2 Test Scenario Additional Trafﬁc

To set up the scenario where a node is used for more

than one bandwidth intensive trafﬁc ﬂow, four nodes

are used (cf. Figure 2). All nodes are located such

that they are in direct communication range. During

the tests, deﬁned additional UDP trafﬁc will be gener-

ated between node 3 and node 4 while the investigated

video stream is transmitted via UDP from the mobile

robot to the user’s PC via node 3. The generated UDP

trafﬁc is used to reach certain load levels at interme-

diate node 3. As in this scenario, node 3 and node 4

are in communication range to all other nodes which

will also cause interferences at the physical layer.

Figure 2: The test setup for additional trafﬁc.

To provide best repeatability of the tests, all nodes

are stationary. Only the additional trafﬁc between

node 3 and node 4 will be varied according to a de-

ﬁned proﬁle. Measured categories are the packet loss

and the packet inter-arrival times. These categories

are measured while the amount of additionally gen-

erated trafﬁc is increased. As reference test, video

transmissions of constant target quality are used and

compared to the packet loss of the transmission with

adaptive quality.

3 HARDWARE

The proposed mechanism was tested in a real outdoor

environment with a wireless ad-hoc network of four

nodes. One is the PC of the operator, one is an Out-

door MERLIN (cf. Figure 3) (Eck et al., 2007), and

two intermediate nodes are MERLIN robots (indoor

version). More details on the scenario are shown in

Figure 2 and a detailed description of the test setup

is given in Section 2. Figure 4 shows the detailed

system setup. All MERLIN robots have a C167 mi-

crocontroller for low-level operations and sensor data

processing, as well as a PC-104 for more complex

and computationally more intensive tasks. The PC-

104 uses a Linux operating system and all nodes are

equipped with 802.11b standard WLAN equipment

(Atheros chip).

To grab the video from an analog camera (ap-

prox. 65 degree ﬁeld of view) an Axis video server

is used. It can grab the video from up to four cam-

eras with a resolution of 768x576 pixels. Dependent

on the conﬁguration and connected clients, a frame

rate of up to 25 images per second can be provided

either as MJPEG or MPEG4 over a TCP/IP connec-

tion. For the described tests the PC-104 is connected

over a cross-link cable to the Ethernet interface of the

VIDEO TRANSMISSION WITH ADAPTIVE QUALITY BASED ON NETWORK FEEDBACK FOR MOBILE ROBOT

TELEOPERATION IN WIRELESS MULTI-HOP NETWORKS

Figure 3: The Teleoperated OutdoorMERLIN Robot.

video server. As nothing else is connected to this Eth-

ernet interface of the PC-104 it can be exclusively

used for the video trafﬁc. For the presented tests four

MJPEG video streams with full resolution are estab-

lished with four different compression rates. MJPEG

as video compression was selected, as MPEG4 com-

pression takes a signiﬁcant longer time on the Axis

server what causes a signiﬁcant delay in the video

stream. Secondly a loss of a packet during transmis-

sion of MPEG4 streams to the robot might lead to

longer set of distorted images because compared to

MJPEG not all frames of the stream contain the full

image information needed. In case of the investigated

scenario, the MJPEG frames are transmitted via UDP

protocol.

4 NETWORK FEEDBACK AND

ADAPTIVE VIDEO QUALITY

The proposed mechanism mainly consists of two

parts: the network feedback, and the adaptive adjust-

ment of the video quality. The mechanism is used for

a simple admission control of the video source and in-

tends to provide the best possible video image quality

considering the current state of the link. The objective

is an efﬁcient use of the available bandwidth without

overloading the route with video trafﬁc to the opera-

tor. Thus, it is not used to increase the link quality

directly but uses the available resources most efﬁcient

and reliable for the operators’ video stream.

4.1 Network Feedback

The network feedback is responsible to transmit the

status of a node to the video source. Therefore, nodes

of the network host a small client program at the ap-

plication layer. This client application is listening in

promiscuous mode at layer 3 of the ISO/OSI model

(IP-layer) and measures the utilization of the wireless

link. All kinds of trafﬁc are monitored: incoming and

outgoing packets, packets for forwarding, and packets

with other nodes in range as destination – basically all

trafﬁc causing the radio link of this node to be busy.

The network feedback client sends very small UDP

packets with an adjustable frequency (in the test setup

10 Hz) and 8 bytes as payload to the video-source if it

is a used hop in the video stream route between video-

source and receiving node. This payload is used to

indicate the status of the corresponding node, either

“normal operation” or “overload situation”. In the be-

ginning, each node is in the “normal operation” mode.

As soon as a certain utilization of the supported band-

width is exceeded, the status of this node switches

to “overload situation”. Important parameters for the

network feedback clients are the feedback frequency

f and the threshold for status determination d. In case

f is too high, too much feedback trafﬁc is generated

which degrades the performance of the network. Even

these packets are very small, too many small packets

with a high sending frequency will have a very bad

effect on 802.11b WLAN and will signiﬁcantly de-

crease the throughput. Thus, the generated feedback

trafﬁc should be limited depending on the interpreta-

tion rate of the video adjustment mechanism and the

selected load window for the wireless nodes. Often

it is also not necessary to run a feedback client on

each network node. For setting parameter d, it should

be considered, that d speciﬁes the percentage of the

nominal bandwidth (e.g. for 802.11b this would be 11

Mbit/sec) which can be used without switching to the

“overload situation” state. The feedback clients mea-

sures packets on layer 3, where the maximum avail-

able bandwidth corresponds to the “goodput” of the

wireless link which is about 75% of the nominal link

bandwidth (e.g. for 802.11b this would be 75% of 11

Mbit/sec).

As the proposed mechanism is used within a net-

work where a link failure can occur at any time, the

measurement and signaling mechanism must be ac-

tive. Thus, link failures and link reestablishing can

be monitored reliably. As the mechanism for video

quality adaptation performs best with a feedback fre-

quency of f = 10 Hz (according to the presented sce-

nario), the generated measurement trafﬁc has a band-

width of less than 0.003 Mbit/sec per measurement

node. To set parameter d, the goodput” of about 7 to

7.5 Mbit/sec (for an 11 Mbit/sec WLAN link) must be

considered. In order to allow a reaction on potential

overload situations while providing the user a video

stream with a bandwidth of 1 to 1.5 Mbit/sec for the

best quality, d is set to 50.

ICINCO 2008 - International Conference on Informatics in Control, Automation and Robotics

Figure 4: System setup.

4.2 Adaptive Video Quality

The Video Quality in the presented system is adapted

according to current state of the ad-hoc route for the

video transfer. The adaption mechanism receives all

status packages from the nodes between two received

frames from the image source, interprets these pack-

ages and selects the quality for the next frames with

a combination of previous status data and the cur-

rent state. Do reduce oscillating behavior in quality

switching near the selected load limit of the nodes a

kind of inertia mechanism for the adaptation process

was integrated. The implemented inertia mechanism

guarantees not to change the image quality whenever

a status of a node changes. It is possible to set a

certain number (cf. Algorithm 1, min/max of iner-

tia counter) of receiving same successive route load

states until the quality is changed. Algorithm 1 shows

this mechanism how the quality for the next frame is

selected according to the received network status mes-

sages.

In the current test setup, four different video qual-

ities are used at a frame rate of 11 frames per second

each. Table 1 shows the average size of one image for

the corresponding image quality level.

A higher number of different quality scales would

also be possible. In the current test setup a minimum

of −3 and a maximum of 3 are selected for the iner-

tia counter. With this value the mechanism reacts in

the worst case after six frames with subsequent over-

load states and in average after three frames. This

keeps the load caused by the video trafﬁc on the dif-

ferent nodes in a certain deﬁned window around the

selected threshold for overload state. In combina-

tion with parameter d of the above described feedback

mechanism, the quality adjustment intervenes as soon

as a node exceeds a radio link utilization of more than

Algorithm 1: Video quality adaptation.

Input: video streams of different quality;

load status messages

initialization;

foreach frame of current selected quality do

if one of the nodes overloaded then

increment inertia counter by one;

else

decrement inertia counter by one;

end

reset node states;

send video frame;

if inertia counter above max then

select lower quality if possible;

set inertia counter to zero;

else

if inertia counter below min then

select higher quality if possible;

set inertia counter to zero;

end

Table 1: Average size of one image per quality level.

Quality minimum low medium high

Size (kbytes) 15 26 34 47

approx. 78% (≈ 50% of nominal bandwidth). This

prevents the node from reaching a utilization of 100%

of the available maximum throughput which would

result in a high packet loss rate due to an increasing

number of packet collisions.

VIDEO TRANSMISSION WITH ADAPTIVE QUALITY BASED ON NETWORK FEEDBACK FOR MOBILE ROBOT

TELEOPERATION IN WIRELESS MULTI-HOP NETWORKS

5 TEST AND RESULTS

In a ﬁrst step, a reference scenario was set up and

measured. Therefore, no network feedback mecha-

nism is used and a mobile robot generates a video

stream which is sent to the PC of the operator as it

is displayed in Figure 2. Between node 4 and node 3,

additional trafﬁc is generated during the different test

phases according to Table 2 to reach a deﬁned load at

intermediate node 3.

Table 2: Generated additional trafﬁc.

Phase generated additional trafﬁc (Mbit/sec)

1 0

2 3,2

3 4

4 4,8

5 5,6

6 6,4

7 7,2

8 8

9 8,8

The results of this reference test are shown in Fig-

ure 5. The x-axis shows the test time in milliseconds.

The left y-axis describes the received frame rate in

frames per second (fps) and the right y-axis displays

the received video data rate in bytes per second (bps)

at the receiving node (operator’s PC).

0 0.5 1 1.5 2 2.5 3

x 10

Frame Rate [frames/s]

Time [ms]

Frame Rate and Traffic without Network Feedback

0 0.5 1 1.5 2 2.5 3

x 10

Video Data [byte/s]

Figure 5: Framerate and Trafﬁc without Network Feedback.

The test started with no additional trafﬁc being

generated. Successively, more and more additional

trafﬁc is generated by switching to the next phase each

20 seconds according to Table 2. After 200 seconds

of test time, the additionally generated trafﬁc is re-

duced by switching back one phase each 10 seconds.

In the beginning of the test – during phase 1 up to the

end of phase 3 – the received frame rate is about 11

fps. After switching to phase 4 at about 60 seconds,

the received video frame rate decreases signiﬁcantly.

The received frame rate between 100 and 200 seconds

drops to 2 − 3 fps while node 3 is overloaded. After

the additionally generated trafﬁc is reduced, the re-

ceived frame rate recovered to 11 fps. Increasing the

additional trafﬁc forces node 3 to an overload situa-

tion. As the bandwidth used by the video stream can-

not be adapted to the new situation, a packet loss of

the video data is inevitable which is shown in Figure

6. The y-axis shows the number of lost packets vs.

the test time on the x-axis.

0 0.5 1 1.5 2 2.5 3

x 10

Packet Loss

Time [ms]

Packet Loss without Network Feedback

Figure 6: Packet Loss without Network Feedback.

Another measured category is the frame inter-

arrival time of the video stream. This is a quite sen-

sitive aspect, as a large jitter (variance of the frame

inter-arrival time) is very irritating for the operator

due to a very unsteady motion of the video image.

Without additional trafﬁc, the frame inter-arrival time

is smaller than 100 ms with a variance close to 0 (cf.

Figure 7) what corresponds to the average frame rate

of 11 fps. After 60 seconds and an additionally gen-

erated trafﬁc of 4.8 Mbit/sec, the frame inter arrival

time increases to more than 400 ms with a variance

of more than 10000 which indicates an unacceptable

video for the operator.

The same test setup is used again – now with the

network feedback and adaptive quality mechanism

(cf. Section 4), which should improve the observed

behavior. In Figure 8, the frame rate and the video

data rate is shown while using an adaptive video qual-

ity together with the network feedback mechanism.

In the beginning, without additional trafﬁc, the mo-

bile robot generates a video stream of about 450000

bytes/sec. During the test, the additionally generated

trafﬁc is increased similar to the test described above.

The implemented mechanism takes care that the video

ICINCO 2008 - International Conference on Informatics in Control, Automation and Robotics

0 0.5 1 1.5 2 2.5 3

x 10

100

200

300

400

500

600

700

800

Frame Inter Arrival Time [ms]

Time [ms]

Frame Inter Arrival Time and Jitter without Network Feedback

0 0.5 1 1.5 2 2.5 3

x 10

2000

4000

6000

8000

10000

12000

Jitter

Figure 7: Frame Inter Arrival Time without Network Feed-

back.

source reduces its generated video trafﬁc to about

300000 as soon as phase 3 (with an additional load

of 4 Mbit/sec) is entered. Increasing the additional

load at node 3 to more than 4.8 Mbit/sec results again

in a reduction of the video trafﬁc (180000 bytes/sec).

During the complete test run, the frame rate stays al-

most constantly at 11 fps as the adaptive video band-

width reduction avoids the loss of video trafﬁc. Also

the frame inter arrival time stays constantly below 100

ms with a jitter of almost 0 (cf. Figure 9).

0 0.5 1 1.5 2 2.5 3

x 10

Frame Rate [frames/s]

Time [ms]

Frame Rate and Traffic with Network Feedback

0 0.5 1 1.5 2 2.5 3

x 10

Video Data [byte/s]

Figure 8: Framerate and Trafﬁc with Network Feedback.

6 CONCLUSIONS

In this work, a mechanism for providing a video

stream over a dynamic multi-hop route with an adap-

tive quality for mobile robot teleoperation is pro-

posed. The mechanism uses a feedback from the net-

work which is generated at dedicated nodes and ad-

justs the image quality to the current communication

0 0.5 1 1.5 2 2.5 3

x 10

100

200

300

400

500

600

700

800

Frame Inter Arrival Time [ms]

Time [ms]

Frame Inter Arrival Time and Jitter with Network Feedback

0 0.5 1 1.5 2 2.5 3

x 10

2000

4000

6000

8000

10000

12000

Jitter

Figure 9: Frame Inter Arrival Time with Network Feedback.

link status. The proper functionality of this adaptive

quality mechanism is tested in teleoperation scenarios

with real hardware under different network load situ-

ations. In situations with a very high link load due

to additional other network trafﬁc, usually the packet

loss rate and the packet inter-arrival time is affected in

a way that reliable and proper teleoperation is not pos-

sible anymore. By adjusting the image quality of the

video stream it is possible to provide a stable video

frame rate for the operator. In fact, the remaining

bandwidth for the video stream is used efﬁciently in

terms of providing a video with a stable frame rate

suitable for mobile robot teleoperation.

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