an evaluation of quality of service for h.264 over 802.11e wlans

8/6/2019 An Evaluation of Quality of Service for H.264 Over 802.11e WLANs

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An Evaluation of Quality of Service for H.264 over

802.11e WLANs

Richard MacKenzie∗, David Hands† and Timothy O’Farrell‡

∗School of Electronic & Electrical Engineering, University of Leeds, Leeds, UK†BT Innovate, British Telecommuncations PLC, Adastral Park, Ipswich, UK

‡Institute of Advanced Telecommuncations, Swansea University, Swansea, UK

Email: [email protected], [email protected], [email protected]

Abstract—802.11 wireless local area networks are now acommon feature in the home. In order to meet the quality of service (QoS) demands for the increasing number of multimediaapplications on these home networks the 802.11e amendmentwas developed. A suitable video coding standard for thesemultimedia applications is H.264 due to its high compressionand error resilience. In this paper we investigate how the qualityof H.264 video is affected as the number of concurrent videostreams sent over a multi-rate 802.11e network is increased.

Several packet mapping schemes are compared. We show thatthe mapping schemes which differentiate video packets based ontheir frame type are more successful at maintaining acceptablevideo quality when congestion occurs, providing a more gradualquality degradation as congestion increases rather than the cliff-edge quality drop that tends to occur with the other mappingschemes. These differentiated schemes are more successful forvideos that do not have a high amount of temporal activity. Wealso identify that impairments caused by congestion tend to occurtowards the bottom of each frame when the flexible macroblockordering (FMO) feature of H.264 is not used but the use of FMOcan reduce this effect.

I. INTRODUCTION

A common feature in the modern home is an 802.11

[1] wireless local area network (WLAN) with an internet

connection. The increase in 802.11 physical layer data rates

along with the availability and affordability of high speed

internet connections has led to an increase in the number of

multimedia internet applications used in the home. There are

a wide range of video applications now available including

low resolution video such as Youtube, video conferencing,

and standard definition and high definition internet protocol

television (IPTV). The home network typically consists of

many devices which may be requesting different services at

the same time. Each service may have its own quality of

service (QoS) requirements. The 802.11e amendment to theoriginal standard was developed to meet the need to be able

to provide QoS over 802.11 networks. Enhanced distributed

channel access (EDCA) allows for service differentiation by

having four parallel queues which can each have different

priorities to access the wireless channel.

This work is funded as an industrial case scholarship agreement betweenBritish Telecommunications PLC (BT) and the Engineering and PhysicalSciences Research Council (EPSRC) under BT/EPSRC case studentshipagreement CT1080038286

The H.264 video coding standard [2] was developed by the

Joint Video Team (JVT) which was formed by a partnership

between the ITU-T Video Coding Experts Group (VCEG)

and the Moving Pictures Experts Group (MPEG). This coding

standard is appropriate for internet video applications due to

its high coding efficiency and is designed to be “network

friendly” for applications which include video telephony, TV

broadcasting and internet streaming [3].Different packets in a video stream can be of varying

importance to the decoding process, so prioritising the more

important packets can help to maintain a better quality received

video. Over an 802.11e network simply mapping packets into

the appropriate EDCA queues can have a significant effect

on the QoS of video applications. There is a great deal of

work related to providing video QoS using EDCA. In [4],

[5] and [6] a variety of traffic mapping schemes have been

investigated. Each scheme prioritises packets based on their

slice type, slice group or partition type depending on how the

video has been encoded. The priority assigned to each packet

determines which EDCA queue each packet is mapped into.

In all of these works the video is of CIF resolution (352x288)and is encoded at bitrates typically well below 1Mb/s. IPTV

services are usually of standard definition television (SDTV)

or high definition television (HDTV) resolutions with bitrates

ranging upwards from 1.5Mb/s. This is reflected in some of

the more recent works focused on IPTV in the home such as

[7], [8], [9] and [10].

In this paper we investigate how many concurrent SDTV

streams can be maintained with acceptable QoS. Several

packet mapping schemes are tested and compared to see which

perform the best. Tests are performed over a range of physical

layer rates to show how each scheme would cope with a

sudden change in the physical layer rate. We identify that

losses tend to occur towards the bottom of each frame if theflexible macroblock ordering (FMO) feature of H.264 is not

used and have results to show that FMO can reduce this effect.

This work on FMO extends the work in [9] by testing a greater

range of both traffic mapping schemes and FMO patterns to

see which can offer the best performance. Peak signal-to-

noise ratio (PSNR) has been a common way to judge video

quality as in [4], [5], [6] and [7]. PSNR is, however, a poor

indicator of perceptual quality [11]. In [8] the video quality

estimation tool described in Annex D of the J.144 standard for



objective perceptual video quality measurement techniques has

been used [12]. We are using the tool described in Annex A

of the same standard. Within the standard these models are

not validated for error impairments such as dropped packets.

Also the various mapping schemes used in our tests produce

different types of video impairments. In [10] subjective quality

values have been collected for the same mapping schemes used

in this paper. The correlation between those subjective scores

and the objective quality scores acquired using the Annex A

model is 0.91.

We have mainly focussed on sending synchronised videos

over EDCA because this is the most challenging scenario when

trying to provide QoS while sending multiple videos over

EDCA. We do, however, show how the system performance

changes when the videos are not synchronised and show that

the overall QoS of the system is not significantly changed.

The rest of this paper is organised as follows. An overview

of the EDCA protocol and the H.264 standard are provided in

sections II and III. Section IV describes the packet mapping

schemes that we will be comparing. The testing procedure

follows in section V. Test results are shown in section VIfollowed by a summary in section VII.

II. EDCA PROTOCOL

The distributed coordination function (DCF), as defined

in the 802.11 standard, provides contention based channel

access using carrier sense multiple access with collision avoid-

ance (CSMA/CA). The 802.11e amendment was developed

to meet the need to be able to provide quality of service

(QoS) over 802.11 WLANs. This amendment specifies the

enhanced distributed channel access (EDCA) function which

provides differentiated, contention-based channel access for

eight user priorities (UPs). Each UP is mapped into one of

four access categories (ACs). Within the 802.11 standard thedescription of the traffic intended for each of the four ACs

are voice, video, best effort and background. These ACs are

named AC VO, AC VI, AC BE and AC BK respectively.

Each AC has its own queue which contends for the channel

using its own EDCA function. Each EDCA function uses its

own set of EDCA parameters which includes an arbitration

interframe space (AIFS[AC]), a minimum and a maximum

contention window value (CWmin[AC] and CWmax[AC]) and

a transmission opportunity (TXOP) limit (TXOP limit[AC]).

AIFS[AC], CWmin[AC] and CWmax[AC] are used in the same

way as the distributed interframe space (DIFS) and the mini-

mum and a maximum contention window values (CWmin and

CWmax) are used by the DCF. The TXOP limit[AC] specifiesthe maximum duration of an EDCA function’s TXOP. If

TXOP limit[AC] = 0 then that EDCA function can only

attempt one frame exchange each time it contends for the

channel. If, however, TXOP limit[AC] > 0 then once that

EDCA function has successfully contended for the channel

it can attempt multiple frame exchanges, separated by short

interframe spaces, without having to contend for the channel

again so long as the total duration of the TXOP does not

exceed the TXOP limit[AC].

III. H.264 STANDARD

The H.264 standard can be described as two distinct layers:

The video coding layer (VCL) and the network abstraction

layer (NAL). The VCL deals with the block based compression

of video samples while the NAL puts the coded video data into

a suitable and flexible form for mapping onto various transport

mechanisms.

The VCL provides efficient compression of the video. Eachframe within the video consists of one or more slices and each

slice can be independently decoded provided that all required

reference frames are available. Each slice will typically consist

of consecutive macroblocks in raster scan order. In this case

the loss of a slice can therefore result in the loss of all coded

information for the entire area of the frame that the lost slice

covered. Error concealment for this area can be very difficult

as all coded information within this area has been lost. Flexible

macroblock ordering (FMO) is an error resilience feature of

H.264 which can help improve the situation of a missing

slice. Each macroblock is assigned to a slice group. Each

slice then contains consecutive macroblocks from within the

same slice group. The pattern of the slice group is defined

by the slice group map. One common slice group map type

is interleaved. The value for run length defines how many

consecutive macroblocks are assigned to each slice group

before switching to the next slice group. Another common

slice group map type is the dispersed map type. Here the

slice groups are scattered. For example with two dispersed

slice groups the slice group map has the appearance of a

checkerboard. If a slice is lost when FMO is being used the

missing macroblocks are more likely to have neighbouring

macroblocks, from other correctly received slice(s), available

which can provide more local information to help improve

error concealment.Frames used for reference are generally considered to be

more important than non-reference frames. If a reference frame

contains errors then these errors can propagate into other

frames that reference it. For this work all frames formed by

B-slices are considered non-reference while frames formed by

either I-slices or P-slices can be used for reference. I-frames

(frames formed by I-slices) are considered the most important

as they make no reference to other frames and are used as

references for the successful decoding of any associated P-

frames or B-frames. In contrast to the relative importance of

frame types, B-frames tend to offer the highest compression

while I-frames tend to offer the lowest compression. This

results in the larger encoded frames tending to be the mostimportant.

Parameter sets, which are created separately from slice

information, contain syntax elements that can apply to the

decoding of many frames. A slice refers to the parameter set

that it uses in its slice header. Therefore the parameter set

information (PSI) must have arrived at the decoder before any

slices that require it. As PSI is required to correctly decode

slice data it should be considered extremely important.

A NAL unit (NALU) consists of a 1B header plus a payload



0

0.1

0.2

0.3

0.4

0.5

0.6

2 4 6 8 10 12

Best Effort

Scheme 1

Scheme 2

Scheme 3

Video Load (Mb/s)

P L R

(a) All slices PLR

0

0.2

0.4

0.6

0.8

1

2 4 6 8 10 12

Best Effort

Scheme 1

Scheme 2

Scheme 3

Video Load (Mb/s)

P L R

(b) I-slice PLR

0

0.2

0.4

0.6

0.8

2 4 6 8 10 12

Best Effort

Scheme 1

Scheme 2

Scheme 3

Video Load (Mb/s)

P L R

(c) P-slice PLR

0

0.2

0.4

0.6

0.8

1

2 4 6 8 10 12

Best Effort

Scheme 1Scheme 2

Scheme 3

Video Load (Mb/s)

P L R

(d) B-slice PLR

Fig. 1: Mean PLR for each mapping scheme

average PLR for I-slices and P-slices only are shown in Fig.

1b and Fig. 1c respectively. Here we find that the default

scheme always has the highest PLR for both I-slices and P-slices, followed by mapping scheme 1. Mapping scheme 2

shows a total protection of I-slices even at the total video load

of 12Mb/s. Mapping scheme 3 has a high protection of I-

slices, although not as high as scheme 2. Scheme 3 does on

the other hand offer a higher protection of P-slices than scheme

2. Fig. 1d shows the average PLR for B-slices only. The

non-differentiated mapping schemes show a relatively good

protection of B-slices compared to how they protect I-slices

and P-slices. The differentiated mapping schemes both show

(a) No packet losses (b) Best Effort

(c) Scheme 1 (d) Scheme 2/3

Fig. 2: Comparison of video streaming schemes with 2Mb/s

‘Fries’ video sequence using different mapping schemes

the same behaviour in their protection of B-slices. For the

differentiated mapping schemes, while the access point is not

congested all B-slices are totally protected but as soon as the

access point becomes congested almost every B-slice is lost.

This results in most B-frames being a frame repeat which

effectively means that during congestion there is a drop in the

frame rate of the video when the differentiated schemes are

used. These PLR results do show that although scheme 1 offers

the lowest PLR overall, the non-differentiated schemes offer

poorer protection to more important slices. The differentiated

schemes however sacrifice the less important B-slices during

congestion in order to maintain a higher protection for themore important I-slices and P-slices.

As shown by the PLR results each mapping scheme offers

different levels of protection for each slice type. This leads to

different characteristics in the decoded video. Fig. 2 compares

the same frame from the ‘Fries’ video sequence encoded at

2Mb/s, for the various mapping schemes. Fig. 2a shows the

decoded sequence when there are no packet losses. Fig. 2b,

Fig. 2c and Fig. 2d show sequences that suffer losses when

the total video load is 8Mb/s for the default scheme, scheme 1

and the differentiated schemes respectively. The differentiated

schemes both output identical frames in this scenario: Both

have perfectly reconstructed I-frames and P-frames, while all

B-slices have been lost resulting in all B-frames being framelosses thus effecting a form of temporal scaling. Therefore the

frame in Fig. 2d, which was a B-frame, shows no noticeable

impairments but is actually a repeat of an earlier frame. Both

non-differentiated schemes do show noticeable impairments.

These impairments tend to be focused towards the bottom of

the frame. The reason is that no flexible macroblock ordering

(FMO) has been used so the macroblocks from each frame are

encoded in raster scan order. The encoded slices are therefore

produced and sent in scan order with each frame transmission



1

2

3

4

5

1 2 3 4 5 6

Best Eff ort Sche me 1

S che me 2 S che me 3

Number of Streams

p M O S

(a) 2Mb/s sequences

1

2

3

4

5

1 2 3

Best Ef fo rt Sche me 1


Number of Streams

p M O S

(b) 4Mb/s sequences

Fig. 3: Video quality results for ‘Fries’ video sequence

1

2

3

4

5

1 2 3 4 5 6

Best Ef fo rt Sche me 1


Number of Streams

p M O S

(a) 2Mb/s sequences

1

2

3

4

5

1 2 3

B es t E ff ort S che me 1S che me 2 S chem e 3

Number of Streams

p M O S

(b) 4Mb/s sequences

Fig. 4: Video quality results for ‘Mobile & Calendar’ video

sequence

resulting in a burst of packets. Packets towards the end of eachburst are more susceptible to losses due to queue overflow or

exceeding the delay bound. This results in more slice losses

towards the bottom of each frame. This effect is more severe

for larger frames which tend to be the reference frames and

as a result the lossy decoded sequences tend to show lots of

impairments towards the bottom of the screen when FMO

is not used. This effect appears as soon as congestion is

experienced when videos are sent with a non-differentiated

mapping scheme while the differentiated schemes avoid this

effect when congestion is first experienced by causing B-slices

to be lost in order to maintain the integrity of the reference

frames.

The video quality scores for the ‘Fries’, ‘Mobile & Cal-endar’ and ‘Rugby’ sequences are shown in Fig. 3, Fig. 4

and Fig. 5. For the 2Mb/s sequences the schemes 1, 2 and

3 all maintain the quality of the original encoded sequences

until the number of concurrent video streams exceeds 3. The

default scheme however shows a slight drop in quality for the

2Mb/s ‘Mobile & Calendar’ sequence when there are just 3

streamed videos. The reason is a combination of the fact that

the default scheme has the lowest channel capacity due to the

EDCA parameter settings along with characteristics of this

1

2

3

4

5

1 2 3 4 5 6

Best Eff ort Sch eme 1


Number of Streams

p M O S

(a) 2Mb/s sequences

1

2

3

4

5

1 2 3

B es t E ff or t S che me 1

S chem e 2 S chem e 3

Number of Streams

p M O S

(b) 4Mb/s sequences

Fig. 5: Video quality results for ‘Rugby’ video sequence

particular sequence. This sequence has relatively high spatial

content with relatively low temporal content compared to the

other sequences. As can be seen from Table II this results in

relatively large I-frames and small B-frames. These larger I-

frames therefore result in larger bursts of packets which aremore prone to losses.

In general both of the non-differentiated mapping schemes

show a cliff-edge drop in quality as soon as congestion is

experienced. This means that as soon as the video load is

high enough to cause congestion the video quality drops

to below 2 on the ACR scale. The differentiated schemes

show better quality performance than the non-differentiated

schemes. For the ‘Fries’ and ‘Mobile & Calendar’ sequences

the differentiated schemes avoid this cliff-edge drop in qual-

ity providing a more gradual degradation in quality. The

differentiated schemes are less effective in producing this

gradual degradation in quality for the ‘Rugby’ video sequence.

This again is due to the video characteristics. The reductionin frame rate caused by the differentiated schemes during

congestion is more noticeable and therefore less acceptable

for high motion content.

This next set of tests looks to solve the issue identified

earlier where impairments tend to appear towards the bottom

of the frame during congestion. The reason that errors tended

to occur towards the bottom of each frame is that flexible

macroblock ordering (FMO) was not used so macroblocks

were encoded in, and the resulting coded slices sent in, raster

scan order. Each sent frame results in a burst of packets where

packets towards the end of the burst are more susceptible to

losses. This effect is more severe for the non-differentiated

schemes which do not protect the packet bursts caused byreference frames as well as the differentiated schemes. We

now test to see if the use of FMO can reduce this effect.

Video encoded without using FMO is now compared to video

encoded using one of three different FMO slice group map

patterns as described in Table V. The run length for the two

interleaved slice group maps is equal to one frame width,

resulting in interleaved rows. The encoder used only applies

FMO to P-frames.

Fig. 6 compares the same frame from the ‘Fries’ video



TABLE V: FMO patterns

Number of Slice group Run length

slice groups map type macroblocks frame width

1 (no FMO) N/A N/A N/A

2 interleaved 45 1

3 interleaved 45 1

4 dispersed N/A N/A

(a) no FMO (b) 2 interleaved slice groups

(c) 3 interleaved slice groups (d) 4 dispersed slice groups

Fig. 6: Comparison of 2Mb/s ‘Fries’ video sequence using

different FMO patterns with mapping scheme 1

sequence encoded at 2Mb/s. The total video offered load is

8Mb/s and mapping scheme 1 has been used. The use of

FMO is successful at preventing impairments from being asconcentrated towards the bottom of the frame. This is done

by spreading lost slice information throughout the screen. By

doing this missing macroblocks are more likely to have some

correctly received neighbouring macroblocks. The decoder

should therefore have more local information for a better

chance of successfully concealing errors in the event of losses.

Fig. 7 shows the video quality for each FMO pattern. These

values are averaged over all 3 video sequences when sent

using mapping scheme 1. When the video load is too low

to cause congestion the sequences encoded with FMO show

a lower quality value than when FMO is not used. This

is due to the lower encoding efficiency experienced when

using FMO [16]. This reduction in quality is, however, notvery significant. When congestion does occur we see that the

quality is very similar whether FMO is used or not, regardless

of which of the tested FMO patterns are used. Although results

are only shown here using mapping scheme 1, this was the

observation for all of our mapping schemes. It is important

to note however that while we find no significant benefit or

loss from using FMO, these results are decoder dependent.

The decoder used for all our tests is designed to be extremely

robust. It uses a fast error concealment method which uses

noFMO FMO - 2interleavedslicegroups

FMO - 3interleavedslicegroups FMO - dispersed

noFMO FMO - 2interleavedslicegroups

FMO - 3interleavedslicegroups FMO - dispersed

1

2

3

4

5

1 2 3 4 5 6

Number of Streams

p M O S

(a) 2Mb/s sequences

1

2

3

4

5

1 2 3

Number of Streams

p M O S

(b) 4Mb/s sequences

Fig. 7: Average pMOS for multiple video streams using

different FMO patterns with packet mapping scheme 1

temporal information whenever possible before reverting to

spatial information. Fig. 6 shows that FMO does succeed in

spreading out errors throughout the screen. This spreading of

errors should mean that each lost macroblock is likely to havemore local information available to help its error concealment.

During congestion this should allow for a decoder with more

advanced error concealment techniques to produce a higher

quality output for a video sequence encoded with FMO than

for a sequence encoded without FMO.

So far, our results have focused on sending synchronised

videos over EDCA. We now see how system performance

varies when the videos are not synchronised and the video

content in each individual test is mixed. We only provide

results in this section for scheme 1 and scheme 3 to represent

the non-differentiated and differentiated schemes respectively.

PLR results are compared between synchronised and non-

synchronised video tests in Fig. 8. While the overall sys-tem PLR remains similar for both synchronised and non-

synchronised video tests, the average PLR per slice type can

be quite different. Fig. 8a shows the PLR per slice type

when mapping scheme 1 has been used. The synchronised

videos receive better protection for B-slices at the expense of

poorer protection to the more important I and P slices when

compared to non-synchronised videos. This demonstrates why

synchronised video tests allow us to test a worst case scenario.

As, already explained, a large frame results in a large burst

of packets which will be more suseptible to losses than a

smaller burst. When there are several synchronised videos

a large frame results in several synchronised large bursts

of packets meaning that large frames have an even higherlikelihood of suffering losses. Fig. 8b shows the PLR per

slice type when using mapping scheme 3. Here there is very

little difference in how the synchronised and non-synchronised

video frames are treated. This shows that a further advantage

of the differentiated mapping schemes is that the treatment

of video packets are less affected by the relative timings of

videos than the differentiated mapping schemes.

Fig. 9 shows the video quality results for the 4Mb/s ‘Fries’

video sequences from the non-synchronised video tests. Fig.



NON-SYNCI-SLICES NON-SYNCP-SLICES NON-SYNCB-SLICES

S YNCI-S LI CE S S YNC P-S LI CES S YNC B-S LI CES

NON-SYNCI-SLICES NON-SYNCP-SLICES NON-SYNCB-SLICES

S YNCI-S LI CE S S YNC P-S LI CES S YNC B-S LI CES

0.0

0.2

0.4

0.6

0.8

1.0

1 2 3Number of 4Mb/s Streams

P L R

(a) Scheme 1

0.0

0.2

0.4

0.6

0.8

1.0

1 2 3Number of 4Mb/s Streams

P L R

(b) Scheme 3

Fig. 8: PLR per slice type for multiple 4Mb/s videos. Synchro-

nised (SYNC) videos compared with non-synchronised (NON-

SYNC) videos

9a shows results when using mapping scheme 1 while Fig. 9b

shows results when using mapping scheme 3. When compared

to the average pMOS values for the synchronised tests inFig. 3b we see that when the videos are not synchronised

the average pMOS is greatly improved for scheme 1 but for

scheme 3 there is no significant improvement. For each test

scenario in Fig. 9 we have shown the mean pMOS along with

the minimum and maximum received pMOS values because

there is a large range of pMOS values experienced from

each test scenario. The reason being that the relative timings

between videos within the same test can affect how well each

video is treated. With the synchronised video tests all sent

videos in a particular test receive very similar treatment so

the mean pMOS value is a good measure for the QoS of the

system. If there is a great variation in the quality of the videos

sent in a particular test, the overall system performance islikely to be judged more by the pMOS of the poorest quality

received video than the average pMOS of all of the videos

in the system. If we compare the minimum pMOS values for

the non-synchronised tests with the average pMOS values for

the synchronised tests we see that the system performance is

very similar. Another noticeable advantage of the differentiated

schemes over the non-differentiated schemes is that during

non-synchronised video scenarios the videos receive more

even treatment when congestion is first experienced. The

tests with synchronised videos show the same loss patterns

as the non-synchronised videos where the errors tend to be

concentrated towards the bottom of the screen when FMO has

not been used.We have already shown, with the results from our earlier

tests, that sending videos through an 802.11e access point

using the differentiated mapping schemes can allow for a more

gradual quality degradation during congestion than using the

non-differentiated schemes. Those tests effected congestion by

increasing the number of concurrent video streams sent over

a single rate physical layer (PHY). Over a wireless network a

common cause of increased congestion can be a reduction in

channel capacity due to PHY rate switching. The following

1

2

3

4

5

1 2 3

MEAN MAX MIN

Number of 4Mb/s Streams

p M O S

(a) Scheme 1

1

2

3

4

5

1 2 3

MEAN MAX MIN


p M O S

(b) Scheme 3

Fig. 9: Video quality for 4Mb/s ‘Fries’ video sequences during

tests with non-synchronised videos

tests compare the same four mapping schemes as earlier,

as described in Table I, for different ERP-OFDM physical

layer rates which can range from 6Mb/s up to 54 Mb/s.

In a home network it is quite possible that there could be

as many as 6 linear TV connections attempted concurrently,

so we have tested for 1 up to 6 concurrent (synchronised)

video streams for each combination of mapping scheme and

PHY rate. Fig. 10 shows the video quality for 1 up to 6

concurrent 2Mb/s sequences. One subfigure is provided for

each of the four mapping schemes. The quality values (pMOS)

are averaged over the quality scores for the ‘Fries’, ‘Mobile

& Calendar’ and ‘Rugby’ sequences. The physical layer rates

tested range from 6Mb/s up to 18Mb/s. From 18Mb/s upwards

all four mapping schemes can send at least 6 concurrent 2Mb/s

videos over the network successfully without them suffering

a reduction in quality.

Fig. 11 shows the video quality for 1 up to 6 concurrent

4Mb/s sequences. Again, one subfigure is provided for each of the four mapping schemes and the quality values are averages

for the quality scores of the ‘Fries’, ‘Mobile & Calendar’ and

‘Rugby’ sequences. The physical layer rates tested range from

6Mb/s up to 54Mb/s. In order to successfully send at least 6

concurrent 4Mb/s videos over the network without suffering

any loss in video quality the physical layer rate must be at least

48Mb/s for the default mapping scheme but only 36Mb/s for

the other three mapping schemes.

When comparing the results for the multiple 2Mb/s and

4Mb/s sequences there are many similar characteristics: The

default best effort scheme, which has the lowest capacity,

shows poorer performance than the other three mapping

schemes. For each physical layer rate, the differentiated map-ping schemes do show a more gradual degradation in quality

than the non-differentiated schemes. Scheme 1 has the highest

channel capacity as it is the only scheme that does not map

any video data into the slow AC BE queue. This can allow the

maximum video quality to be maintained for a slightly higher

load than the differentiated schemes. This effect can, however,

only be seen with the 9Mb/s physical layer rate results.

In general the differentiated schemes tend to show the

best performance and also allow for a more gradual quality



1

2

3

4

5

1 2 3 4 5 6


p M O S

(a) Best Effort

1

2

3

4

5

1 2 3 4 5 6


p M O S

(b) Scheme 1

1

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3

4

5

1 2 3 4 5 6


p M O S

(c) Scheme 2

1

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3

4

5

1 2 3 4 5 6


p M O S

(d) Scheme 3

18Mb/s6Mb/s 9Mb/s 12Mb/s 18Mb/s6Mb/s 9Mb/s 12Mb/s

Fig. 10: Average pMOS for multiple 2Mb/s streams sent using different physical layer datarates

1

2

3

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5

1 2 3 4 5 6


p M O S

(a) Best Effort

1

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3

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1 2 3 4 5 6


p M O S

(b) Scheme 1

1

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p M O S

(c) Scheme 2

1

2

3

4

5

1 2 3 4 5 6


p M O S

(d) Scheme 3

54Mb/s

6Mb/s 9Mb/s 12Mb/s 18Mb/s



24Mb/s 36Mb/s 48Mb/s

Fig. 11: Average pMOS for multiple 4Mb/s streams sent using different physical layer datarates

degradation than either of the non-differentiated schemes. This

gradual quality degradation can be a great benefit if there

is a sudden drop in the physical layer rate. For example

if we are streaming five 4Mb/s videos through an access

point with a physical layer rate of 36Mb/s then all mapping

schemes work successfully and maintain the maximum video

quality. If the physical layer rate drops to 24Mb/s all mapping

schemes suddenly suffer a drop in video quality. For the

default best effort scheme the quality instantly drops to below

2 on the ACR scale, the quality using scheme 1 is around

2.5, while the differentiated schemes only drop as low as

around 3.5. A further drop in physical layer rate to 18Mb/s

sees the quality using scheme 1 fall well below 2, while the

differentiated schemes are still able to stay above 2. It is this

ability to maintain a better quality during a sudden increase

in congestion that highlights the benefit of these differentiated

mapping schemes.

Both differentiated mapping schemes show similar perfor-



mance with scheme 2 being the best. A real time video service

is almost certain to have an accompanying audio stream.

Audio streams are typically very low data rates relative to

the video stream. The recommended EDCA parameter sets

were designed with the intention that real time audio streams

are mapped into AC VO so that they do not have to compete

with the higher rate video streams which should be mapped

into AC VI. However, scheme 2 already maps I-slices into

AC VO. This means that scheme 2 should experience a drop

in audio quality well before scheme 3. While both schemes

map PSI into AC VO this low amount of data will have little

effect on audio streams. The overall quality of service for an

audiovisual service is a combined effect of both the quality

of the received video and the received audio [17]. So in order

to maintain a robust video streaming service while trying to

avoid the loss of audio information, which should be mapped

onto AC VO, we would recommend the use of traffic mapping

scheme 3.

VII. SUMMARY

In this paper we have compared streaming multiple con-

current H.264 streams through an 802.11e access point for

several traffic mapping schemes. We have shown that all

mapping schemes used provide better performance than the

default best effort service. The best performing mapping

schemes differentiate packets based on their slice type and

allow a more gradual drop in video quality as congestion is

increased, avoiding the much steeper decline in quality that

occurs with the other schemes. We have also shown how

these differentiated schemes can offer a benefit over the non-

differentiated schemes during a drop in physical layer rate.These differentiated schemes are less effective, although still

better performing than the non-differentiated schemes, when

the video content has a high amount of temporal activity.

We have shown the difference between sending multiple

synchronised videos with multiple non-synchronised videos.

We have shown that while sending synchronised videos is

a worst case scenario in terms of the average perceptual

quality of a received video, the overall system QoS is not

significantly changed. We have also identified that without

the use of FMO impairments caused by congestion tend be

concentrated towards the bottom of the frame. By distributing

errors, which are often severe, across the frame by the use of

FMO the video quality remains similar for an H.264 decoderthat does not have sophisticated error concealment techniques.

Further investigation is needed to confirm whether or not this

spreading of severe loss errors across the frame will in fact

allow a decoder with advanced error concealment techniques

to conceal errors more effectively than if FMO is not used.

This does however highlight the benefit of the differentiated

mapping schemes which are a simple and effective way to

improve the robustness of video streams on a congested

network without the need for a sophisticated decoder.

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an evaluation of quality of service for h.264 over 802.11e wlans

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