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J. Nightingale et al.: Optimised Transmission of H.264 Scalable Video Streams over Multiple Paths in Mobile Networks 2161

Optimised Transmission of H.264 Scalable Video Streams

over Multiple Paths in Mobile Networks

James Nightingale, Student Member , Qi Wang, Member , and Christos Grecos, Senior Member , IEEE

Abstract — Consumer demand for portable wireless

devices such as smartphones or tablets capable of receiving

high quality video content has risen sharply in recent years.

This paper considers the real-time delivery of streamed H.264

Scalable Video Coding (SVC) content to users of such devices

within mobile networks (e.g. situations where they are part of

a group moving together on a bus or a train). We propose a

novel scheme for the multipath delivery of H.264 SVC content

to users in multihomed mobile networks .By implementing our

scheme on a realistic testbed, we show that it offers a

significant improvement in received video quality over

previously proposed alternative schemes1.

Index Terms — H.264 SVC, Mobile Networks, Multihomed,Multipath Streaming.

I. INTRODUCTION

The use of a wide range of sophisticated personal wireless

devices (laptop, netbook, smartphone, tablets etc.); is

becoming commonplace in society. Users of such equipment

will expect to be able to make full use of a device’s

capabilities in everyday situations including when travelling

on public transport. There are numerous technical challenges

associated with streaming media content to nomadic users in

public transport situations. These include mobility

management, the low available bandwidth on some public

networks and the lack of universal coverage by any individualnetwork.

An emerging wireless networking paradigm known as

Mobile Networks [1] has been developed to address the

mobility requirements of groups of users (or network devices)

travelling together in unison. Mobile devices, when acting as

nodes in a mobile network, no longer directly connect to the

user’s ISP but rather to a local device within the mobile

network that handles mobility on behalf of all nodes.

Multihomed mobile networks are those in which multiple

heterogeneous access paths to the network can be accessed

and used simultaneously.

Scarcity of available bandwidth on public access networks

may mean that there is insufficient bandwidth on any singlenetwork path to ensure delivery of a media stream from server

to client. The recent introduction of the H.264 Scalable Video

Coding [2] extension to the H.264 Advanced Video Coding

Standard (AVC) [3] provides the ability to adapt video

streams in response to varying network conditions.

1 James Nightingale, Qi Wang and Christos Grecos are with the Audio

Visual Communications and Networks Group, School of Computing,

University of the West of the Scotland, Paisley, United Kingdom (e-mail:

[email protected]).

Another way in which bandwidth limitations can beovercome is by using the aggregated bandwidth of all

available network paths from streaming server to client in

order to maximize the throughput. A number of schemes

have been proposed to make best use of this aggregated

bandwidth to ensure the delivery of MPEG2 and MPEG4

streams to client nodes which are either static or nomadic

but not part of a mobile network. Any such scheme must

take account of both available network path conditions

and the characteristics of the media stream itself when

deciding which path(s) to use for streaming and should

ensure that the simultaneous use of multiple paths does

not lead to a higher incidence of out-of-sequence packet

reception at the client.In this work we apply typical multipath streaming

algorithms to H.264 SVC and practically implement them

on a testbed offering a realistic multihomed mobile

networks environment. We empirically evaluate them and

propose an optimised multipath streaming algorithm for

use in this previously unconsidered environment. As

previous work on multipath streaming schemes has not

focused on this environment, some practical factors that

affect the performance of multipath streaming algorithms,

if applied to multihomed mobile networks, have not been

considered. The most significant factors are the non-

negligible addition of tunnelling overheads in mobile

networks and the path switching delay in multihomedmobile networks.

Our technical contributions are multi-fold and are

outlined as follows. Firstly, in our implementation, packet

priority weighting schemes designed for AVC in existing

work have been extended to include the rich scalability

vectors of SVC and to utilise the by far greater degree of

granularity offered by SVC over AVC. Secondly we

mitigate the previously unconsidered tunneling and path

switching overheads encountered in mobile networks.

Lastly, we seek to reduce path switching frequency (and

associated delay) by both scheduling at an RTP packet

level; rather than the previously proposed IP level; andtrading off bandwidth aggregation in favour of reduced

path switching frequency.

The rest of the paper is organized as follows. Related

work is reviewed in Section II. Section III describes our

proposed scheme, while our testbed and implementation is

presented in Section IV. Section V presents the results of

our empirical comparison of multipath scheduling

algorithms and Section IV concludes the paper.

Contributed Paper Manuscript received 10/15/10Current version published 12/23/10

Electronic version published 12/30/10. 0098 3063/10/$20.00 © 2010 IEEE

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IEEE Transactions on Consumer Electronics, Vol. 56, No. 4, November 20102162

II. R ELATED WORK

This work brings together aspects of research in both the

computer networking community and the real-time video

processing community.

A. Mobile Networks

Fig. 1. A typical multihomed mobile networks topology.

The Network Mobility (NEMO) [4] protocol, a further

development of MIPv6 [5], allows groups of users travelling

together to connect to a local device called a Mobile Router

(MR) that handles the mobility management requirements of

all of its attached mobile network nodes (MNN). Fig. 1 shows

a simplified topology of a multihomed mobile network.

Mobility is handled by two NEMO mobility agents, one of

which is the Home Agent (HA) residing in the MR’s home

network and the other is at the MR itself . Any data packets

destined for a node in the mobile network are routed via the

HA where they are encapsulated for tunnelling to the MR.

Packets are then transmitted over a bi-directional tunnel

between HA and MR. At the MR the encapsulation header is

removed and the packets are forwarded to the MNN. The

tunnelling process from HA to MR adds an additional network

overhead to each IP packet transmitted and as the traffic from

all network nodes must pass through the link from the MR to

the radio access point, this link can become the bottleneck on

the transmission path. In multihomed mobile networks the

mobile router has multiple network interfaces, each of which

is connected to a different radio access network. These access

networks, may employ a variety of heterogeneous radio

technologies or simply provide homogeneous connections to

different service providers. The MR is able to makesimultaneous use of all attached access networks, and thus

circumvent the single link bottleneck.

We have previously addressed the issue of path selection in

multihomed mobile networks by proposing a scheme [6], [7]

to provide an always best connected path in such networks. In

that scheme, we provided a general-policy driven mechanism

that exploits current path condition metrics and application

specific rules to determine the current best path for an

application flow. The path is changed as required at the

application flow level per application. In this paper, we

greatly extend our previous work by designing and

implementing a novel RTP packet level scheduling and

switching scheme for SVC streaming.

B. Scalable Video Coding

H.264 SVC allows the encoding of video sequences as a

number of sub-streams. In SVC a stream consists of an AVC

compliant base layer, providing a minimum quality of video,

and a number of enhancement layers, which improve thequality of the received stream. The three-dimensional

scalability of SVC (spatial, temporal and quality), can be used

for network or terminal adaptation of streams. To conserve

network resources, a sender only transmits those layers that a

client node is capable of processing. If there is insufficient

bandwidth to deliver the entire stream network adaptation may

employed. This will drop higher enhancement layers, reducing

the bandwidth requirement and thus ensuring delivery of the

base layer and lower enhancement layers. Providing the user

with an acceptable quality of video and making efficient use

of available bandwidth.

A number of schemes have been proposed for the adaptionof SVC streams in response to varying network conditions [8],

[9]. In [8] an Adaption Decision Taking Engine (ADTE) is

placed at the streaming server. It makes real-time stream

adaption decisions based on client capability and network path

data. This data is contained in MPEG-21 DIA [10] messages

sent from the client. The scheme proposed in [9] considers

both network conditions and device energy consumption.

Neither of these has considered the delivery of SVC in

multihomed mobile networks. While work in the IETF to

provide a standard for the delivery of SVC over RTP [11] is

nearing completion, no freely available streaming servers or

playback clients for SVC are currently available. In the

absence of such standards and tools, we explore and further develop the Scalable Video Evaluation Framework [12]

(SVEF) for the empirical evaluation of real- time SVC

streaming.

C. Multipath Streaming Algorithms

A number of Quality of Service (QoS) related schemes have

been proposed to improve delay sensitive media streaming

over IP based networks, some of which have considered the

use of the aggregated available bandwidth of all network paths

from streamer to client. Media-aware schemes such as those

proposed by Chebrolou and Rao [13], [14] and Jurca and

Frossard [15]; take account of the characteristics of both the

media stream and current conditions on available network

paths when deciding how to distribute a video stream across

multiple paths. Of these schemes, only [15] considers a

scalable video format; however, [15] is a simple generic

format rather than the sophisticated three dimensional

scalability of SVC. These multipath streaming algorithms

make path selection and scheduling decisions on a per packet

basis at the IP level and in the case of [15] drops packets that

cannot be successfully scheduled. This differs significantly

from schemes such as [8] where adaptation is at a per SVC

layer granularity. Multipath scheduling and scheduling can

lead to out-of-sequence delivery of packets at the client, which

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then requires a larger input buffer; this can be a limiting factor

for resource-constrained mobile devices. In Earliest Delivery

First (EDF) [13] and Earliest Delivery Path First (EDPF) [14]

algorithms, the network metrics of available bandwidth and

delay on each path and the size of each packet are used to

estimate the arrival time at the client. A packet is always sent

on the path offering the earliest arrival time, thus reducing

out-of-sequence delivery. The heuristic load balancing

algorithm (LBA) from [15] also performs stream adaptation inresponse to changing network path states by only transmitting

those packets that are estimated to arrive at the client in time

to be of use in the decoding process. Additionally, LBA

conserves bandwidth by dropping packets that cannot be

decoded because they rely (for decoding) on a previous packet

that has already been dropped. A packet prioritisation scheme

in LBA gives a higher weighting to I frames over B and P

frames and also to base layer packets over enhancement layer

packets. The LBA scheduler sorts packets according to

priority weighting, and sacrifices lower priority packets to

ensure the delivery of those with a higher priority.

Placement of the scheduling mechanism in LBA is at the

streaming server equipped with multiple network interfaces,each of which provides a completely independent path to the

client. In EDPF, the mechanism is placed at the HA. Both

approaches have limitations. In the case of LBA it is more

likely that, in a realistic network setting, the point of

divergence of paths will be at a router rather that at the server

itself. With EDPF the issue is of placing a computationally

intensive mechanism on the HA, which is a router rather than

a sever and has already been occupied with mobility

management tasks. Moreover, if a bi-directional streaming

process is considered, the scheduling burden would fall on the

resource constrained MR when the MNN is acting as the

streamer.

EDPF assumes a stable negotiated bandwidth for theduration of a streaming session, while LBA and the scheme in

[8] consider dynamic path conditions.

III. PROPOSED ALGORITHM

We propose a path selection and packet scheduling

algorithm for use in multihomed mobile networks in which we

take account of the previously unconsidered network

overheads associated with this environment.

A. Factors Influencing Path Choice

Each of the algorithms discussed in section II considers a

different set of factors when making a path selection or packet

scheduling decision. EDPF only considers the size of a media

packet together with available bandwidth and delay but ignore

packet dependencies and the unequal importance of packets in

a video stream. While LBA does consider these additional

factors and also queuing times at intermediate routing nodes,

it was targeted at wired networks and did not address mobility

issues. The first additional factor that we consider is the

mobility-related networking overhead added to each IP packet

transmitted by the streamer. If the size of an RTP packet

exceeds the Ethernet maximum transmission unit (MTU) size

of 1500 bytes it will be fragmented into several IP packets,

each of which will have its own mobility related network

overhead added.

Fig. 2. RTP packet sizes in the 30 fps of the Soccer sequence. The majority

of RTP packets are larger than the Ethernet MTU. Almost 5% are more

than ten times the MTU size.

The size of the additional overhead added to each packet is

dependent on the level of nesting within a mobile network.

Each packet is tunnelled from the HA to the MR and has an

encapsulation overhead added to the packet’s overall

transmitted size. In our experiments, 58 % of packets in the

Bus sequence at 30 fps were greater than the MTU, as can be

seen in Fig. 2, and this was higher at 70% for the 30 fps

Soccer sequence. The second additional factor that we

consider is the path switching delay in multihomed mobile

networks. In previous work [6], [7] we proposed a path

switching mechanism to support our Always Best Connected

path approach. In this work we have further implemented this

switching mechanism as a path control module at the

streaming server and a client module at the HA where path

switching takes place. As a precursor to our main streaming

experiments, we measured the delay introduced by each path

switching operation as shown in Fig. 3. It was found that the

average switching delay in our realistic mobile networks

testbed is 137ms.

Fig. 3. Delay introduced by path switching using the Bus sequence at

30fps.

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By extending this scheme to all three dimensions such that

weighting = ( .Lid)+( .Tid)+Qid, we provide a scheme that

reflects the full granularity of SVC.

In this work, we applied only a small number of the

available weightings rather than deploying the full granularity

described above. We did this in order to provide the ability to

map our weightings to the scheme used in [15] that is reliant

on I, P and B frame types rather than the SVC scalability data.

We are therefore able to provide a fair comparison with

representative algorithms from literature and evaluate effects

of the other novel components in our architecture. We

performed this mapping in our pre-processing module.

It should also be noted that the default JSVM encoder [16]

output order for SVC streams already offers a degree of

prioritisation of NAL units by sending those pictures (within a

GOP), which are required for prediction by others in the GOP

earliest to try and ensure their delivery. This is shown in Fig.

5.In LBA [15] all ancestors of a packet are identified. If any

packet upon which the current packet relies for decoding has

not already been scheduled, LBA attempts to do so. If any

ancestor is cannot be scheduled, the current packet is dropped.The exact method of determining a packets ancestors in real-

time is not discussed in [15].

Our practical implementation limits the ancestor checking

function to within the current window of knowledge (read

ahead window) of the streamer. To identify a packet’s

ancestors, we make use of SVC scalability information, frame

number and GOP size data. The frame number and scalability

information of any NAL unit contained in dropped packet

within the current window (typically 1 or 2 GOP’s in length)

is stored in memory. By comparing the frame number, Lid,

Tid and Qid of the current packet to those of failed packets in

the current window, we can establish if a packet’s ancestorshave been scheduled without the need for the expensive

recursive searching method employed in [15]. As the read

ahead window is small, the data stored for each packet is only

5 bytes and only data for packets dropped in the current

window is stored; the memory overhead in our scheme is

minimal, making it suitable for resource-constrained devices.

Fig. 5. Inputs considered by each algorithm and possible scheduling

outcomes that can be made.

Fig. 6. Path Monitoring & Control.

Since current path measurement tools generally need a

number of round trip times to accurately estimate path

conditions and the authors of EDPF and LBA have assumed

the instant availability of current path metrics, we designed

and implemented a path control mechanism, which makeschanges to path conditions within the core network and

reports any changes (in less than 10ms) to the streaming

server. This is similar to the virtual choke point in [17]. Fig 6

provides a diagram of the signalling involved in this path

monitoring and control mechanism.

When a path change is required, the path switching control

module at the streamer signals the new path number to the

client module on the HA. Where the change is implemented

and acknowledged. The controller then updates the scheduler

with the time taken for the path switching. This allows the

time at which a path next becomes free to be accurately

estimated when calculating the arrival time of the next packet.

Signalling for this is shown in Fig. 7. A diagrammatic

representation of our OPSSA algorithm is given in Fig. 8.

Fig. 7. Path Switching Mechanism

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Fig. 8. Optimized scheduling algorithm.

IV. IMPLEMENTATION

Fig. 9. Topology of our multihomed mobile networks testbed.

We provide a practical Linux user space implementation of

three path selection and scheduling algorithms (EDPF, LBA

and the proposed OPSSA) on a realistic multihomed mobilenetworks testbed. Our testbed, the topology of which is shown

in Fig. 9, consists of standard PCs running Ubuntu Linux for

the video streaming server, the mobility-management home

agent, the core routers, the mobile network router and mobile

network clients in our testbed. Two paths are provided

between the video streaming server and the multihomed

mobile network. Each path consists of a 100Mbps Ethernet

wired link incorporating a core router running wide-area

network emulation and path monitoring modules and an IEEE

802.11g wireless link offered by a modified Linksys

WRT54GL wireless router. All PCs used in the testbed have

3.4 GHz Pentium 4 processors with mobility agents and core

routers having 1 GB of RAM and the end nodes (streaming

server and mobile client) having 512 MB of RAM. Mobility

management is provided by NEMO running at both the home

agent and the mobile router.

Fig. 10. Testbed path switching and monitoring overview.

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Fig. 10 gives an overview of the design of our testbed. We

incorporate mechanisms to provide path switching and path

monitoring functions which, when combined with our

scheduling implementation, provide an application-specific

(SVC streaming) instance of the Network Selection Algorithm

[6] (NSA) or always best connected service [7] introduced in

our previous work.

SVEF [12] is an open source testing and evaluation tool for

single path IPv4 transmission of stored SVC content. As nextgeneration networks use the IPv6 version of the Internet

Protocol, we have rewritten the network interface of SVEF to

support both IPv4 and IPv6 address families and the MIPv6

based mobility management software used in mobile

networks. The SVEF trace file is extended by rewriting the

existing pre-processing module to calculate the relative

decoding deadline (in relation to the first NAL unit in a

stream) and priority weighting of each NAL unit. The pre-

processor permits the easy sorting of the trace file to test a

number of algorithms and streaming scenarios.

V. EVALUATION

All three algorithms (EDPF, LBA and the proposed

OSPSA) that have been implemented on our mobile networks

testbed are empirically compared. Publicly available video

sequences were encoded using the JSVM reference software

and the performance of each algorithm compared in terms of

packet delivery statistics and statistical video quality metrics.

Versions of the Soccer sequence with 4CIF (704x576)

resolution were used at frame rates of 30 and 60 fps, together

with versions of the Bus sequence with QCIF (176x144)

resolution and frame rates of 15 and 30 fps. Each was

encoded with a base layer and two MGS scalability layers.

We implemented a comprehensive logging scheme at both

the streamer and client. The streamer recorded packet size,

identity, SVC scalability data, mobility overhead and the

scheduling decision for each packet. Each path switching and

the delay introduced was also recorded. At the client the

arrival time relative to the first packet in the stream is

recorded.

To understand the effect on performance of added mobility

overheads, we compared two versions of our optimised

algorithm, one which mitigated the additional overheads and

one which used the payload size only when calculating

expected the arrival time of a packet. We used both sequences

across the full range of frame rates. The mobility overhead

added to the Soccer sequence at 30 fps increased the size of

the stream by 4.83% the added overhead will be even more

significant in nested mobile networks with hierarchical mobilerouters. When the overheads were not taken into account, the

number of packets arriving at the client that were unusable at

the client (either due to arriving after their decoding deadline

or because an ancestor had arrived after its decoding deadline)

increased on average by 6.1%. The Bus sequence, which has

fewer large packets, performed better than the Soccer

sequence. The number of base layer packets failing to arrive

on time also increased (from 0.05% to 0.8 % for the Soccer

sequence) and received video quality was reduced. The PSNR

of the Soccer sequence was reduced by 0.36 dB and the Bus

sequence by 0.14 dB. We, therefore, have shown that the

effects on video quality of added mobility overheads in mobile

networks are significant.

Fig. 11. Path switching frequency (data collected at the streamer).

Path switching frequency is influenced by a number of

factors, apart from the algorithm used. These include the

relative difference between paths in terms of available bandwidth and delay and the size of the RTP packets being

scheduled as larger packets have longer transmission times. In

our experiments, we found that packet size influenced path

switching frequency, effective bandwidth aggregation rate and

was the largest single factor (when translated to transmission

time) even on paths with higher available bandwidth and low

delay. In our algorithm, we trade off effective bandwidth

aggregation and reduced path switching frequency. This

strategy, as can be seen in Fig 11, is more effective when

paths characteristics are unequal. OPSSA outperforms all of

the others, including a path switching compensated version of

LBA that we implemented for comparative purposes andconfigured with a 137ms path switching compensation value.

The cumulative effect of unmitigated path switching delays

can be clearly seen in Fig. 12, where packets delivered via

EDPF and LBA implementations, which do not consider the

switching cost, very quickly begin to deliver packets later than

their decoding deadline and thus rendering them useless in the

decoding process.

Fig. 12. Comparison of packet arrival times at client for EDPF, LBA and

OPPSA using a 30 fps Bus sequence.

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Fig. 13. Packets arriving at the client, those that are within their decode

window and those which are useable in the decode process.

The results clearly demonstrate OPSSA delivers themajority of packets within the decoding deadline. The cyclic

nature of the decoding deadline on the graph results from the

way in which packets are sorted by priority weighting and

then decoding deadline on a per read ahead window basis. It

should be noted that only those packets that arrived at the

client for each frame are included in the graph. Fewer packets

are delivered by OPSSA than either EDPF or LBA due to the

dropping at the streamer of packets that were estimated not to

arrive on time.

From Fig 13 it can be observed that OPSSA provides the

highest number of useable packets to the client while also

sending the lowest number of packets. This effect was more

pronounced on high differential paths and on the Soccer

sequence where the packet size distribution in the stream is

less equal than with the Bus sequence. Even if a packet arrives

on time it can only be used if packets that it depends on also

arrived on time.

The received video quality, when measured using the

statistical Peak Signal to Noise Ratio (PSNR) metric is higher

for OPSSA than for the other schemes. EDPF and LBA do not

perform satisfactorily as they do not consider the additional

overheads and switching costs. Path switching compensated

LBA performs better but still has a higher path switching

frequency and does not consider the mobility overheads.

Although it provides an acceptable quality of received video,it does not perform as well as OPSSA.

The results of our received video quality measurements are

shown in Fig. 14. The PSNR of both LBA and EDPF quickly

fall below acceptable limits while OPSSA and path switching

compensated LBA perform significantly better, confirming

our hypothesis that path switching overhead is a significant

limiting factor that effects multipath streaming in multihomed

mobile networks. One of the limitations of the current version

of the JSVM decoder is its inability to correctly deal with

Fig. 14. Video quality comparison measured using PSNR

packets that have unmet dependencies. We, therefore use the

SVEF framefiller mechanism to firstly generate a filtered copy

of the video sequence containing only those packets that

correctly arrived at the client on time to be of use in the

decoding process and had all ancestor packet available at the

decoder. The simple SVEF frame filler routine is then applied

to conceal missing parts of the sequence. This reconstructed

video sequence is compared to the original sequence using the

JSVM reference software PSNR comparison tool.

VI. CONCLUSIONS

In this work, we have introduce a scheme for the delivery

of H.264 SVC streams across multiple paths in multihomed

mobile networks. We have demonstrated that mobility

overheads and path switching costs are significant factors thatmust be considered when distributing a stream across multiple

paths in this environment. Furthermore, we have shown that

our algorithm (OPSSA) outperforms representative algorithms

from literature (in terms of PSNR) when implemented in this

context. Our experiments were performed on a testbed

environment with realistic switching costs of; on average

137ms.

By trading off bandwidth aggregation against a reduced

level of path switching, our scheme; provides a higher quality

video stream to the client. Improvements range from 0.4 to 1.0

dB (dependant on sequence and encoding) for equal paths, to

in excess of 2.3dB for paths where one has a substantiallyhigher capacity than the other.

Our work has substantially extended previous

representative algorithms for use with H.264 SVC streams in

multihomed mobile networks. We have provided both an SVC

packet prioritisation scheme suitable for use with multipath

streaming algorithms and a low cost means of determining, if

a packet’s ancestors have been scheduled in real time. The

proposed algorithm OPSSA, is suitable for use on resource-

constrained devices.

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R EFERENCES

[1] T. Ernst, K. Uehara, and K. Mitsuya, “Network mobility from the

InteretCAR perspective.” in Proc. 17 th IEEE Int. Conf. Adv. Inf. Netw.

Appl., Mar. 2003, pp. 19-25.

[2] H. Schwarz, D. Marpe, and T. Wiegand, "Overview of the Scalable

Video Coding Extension of the H.264/AVC Standard," IEEE

Transactions on Circuits and Systems, vol. 17, 2007, pp. 1103-1120.

[3] T. Weingand, G. Sullivan, G. Bjontegaard, and A. Luthra, “Overview of

the H.264/AVC video coding standard,” IEEE Trans. Circuits Syst.

Video Technol.,vol. 13, no. 7, pp. 560-576, July 2003.[4] V. Devarapalli, R. Wakikawa, A. Petrescu, P. Thubert, “Network

mobility (NEMO) basic support protocol,” Internet RFC: RFC 3963.

[5] D. Johnson, C. Perkins, and J. Arkko, “Mobility support in IPv6,”

Internet RFC: RFC3775.

[6] Q. Wang, R. Atkinson, and J. Dunlop, “Design and evaluation of flow

handoff signalling for multihomed mobile nodes in wireless overlay

networks,” Computer Networks, vol. 52, Jun. 2008, pp. 1647-1674.

[7] Q. Wang et al, ”QoS-aware network-supported architecture to distribute

application flows over multiple network interfaces for B3G users,”

Wireless Personal Communications, vol. 48, 2009, pp.113-140.

[8] H. Choi, J. W. Kang, J-G. Kim, “Dynamic and Interoperable Adaption of

SVC for Q0S-Enabled Streaming, ” IEEE Transactions on Consumer

Electronics, vol. 53, no. 2,pp. 384-389, May 2007.

[9] H. Lee, Y. Lee, J. Lee, D. Lee, and H. Shin, "Design of a mobile video

streaming system using adaptive spatial resolution control," IEEE

Transactions on Consumer Electronics, vol. 55, no. 3, 2009, pp. 1682-

1689.

[10] ISO/IEC PDTR 21000-12:2004, “Information technology – Multimedia

framework (MPEG-21) Part 12: Test Bed for MPEG-21 Resource

Delivery,” 2004.

[11] S. Wenger, Y.-K. Wang, T. Scheirl, and A Eleftheriadis, RTP payload

format for SVC video”, Internet Draft, draft-ietf-avt-rtp-svc-22, August

2010, work in progress.

[12] A. Detti, G. Bianchi, C. Pisa, F.S. Proto, P. Loreti, W. Kellerer, S.

Thakolsri, and J. Widmer, "SVEF: an open-source experimental

evaluation framework for H.264 scalable video streaming," 2009 IEEE

Symposium on Computers and Communications, 2009, pp. 36-41.

[13] K. Chebrolu and R. Rao, "Bandwidth aggregation for real-time

applications in heterogeneous wireless networks," IEEE Transactions on

Mobile Computing , vol. 5, 2006, pp. 388–403.

[14] K. Chebrolu and R. Rao, "Selective frame discard for interactive video,"

2004 IEEE International Conference on Communications (IEEE Cat.

No.04CH37577), vol. 00, 2004, pp. 4097-4102.[15] D. Jurca and P. Frossard, "Video packet selection and scheduling for

multipath streaming," IEEE Transactions on Multimedia, vol. 9, 2007,

pp. 629–641.

[16] J. Reichel, H. Schwarz, and M. Wien, Joint Scalable Video Model

11(JSVM 11), Joint Video Team, Doc. JVT-X202, Jul. 2007.

[17] G. Bianchi, A. Detti, P. Loreti, C. Pisa, F. Proto, W. Kellerer, S.

Thakolsri, and J. Widmer, "Application-aware H. 264 scalable video

coding delivery over wireless LAN: Experimental assessment," Cross

Layer Design, 2009. IWCLD'09. Second International Workshop on,

IEEE, 2009, p. 1–6.

BIOGRAPHIES

James Nightingale (S’09) received the BSc degree in

Network Computing from Edinburgh Napier

University, UK and the BSc (Hons) degree in Computer

Networks from the University of the West of Scotland,

UK, where he currently a Ph.D. student. His research

interests include mobile networks, multihoming and

video streaming techniques.

Qi Wang (S’02-M’06) Dr Qi Wang is a Lecturer in

Computer Networking with the University of the West

of Scotland (UWS), UK. Previously, he was a

Postdoctoral Research Fellow with the University of

Strathclyde, UK, and a Telecommunications engineer

with the State Grid Corporation of China. He received

his PhD in Mobile Networking from the University of

Plymouth, UK, and his BEng and MEng degrees from

Dalian Maritime University, China. Recently, he has been involved in the

European Union FP6 MULTINET project and the UK EPSRC DIAS

project. His research interests include Internet Protocol networks and

applications, diverse wireless networks, mobility management,

multihoming support and intelligent network selection, and cross-layer design. He is a member of IEEE, and on the technical programme

committees of a number of international conferences.

Christos Grecos (M’01-SM’06) Prof Christos Grecos

is a Professor in Visual Communications Standards,

and Head of School of Computing, the University of

the West of Scotland (UWS), UK. He leads the Audio-

Visual Communications and Networks Research Group

(AVCN) with UWS, and his research interests include

image/video compression standards, image/video

processing and analysis, image/video networking and

computer vision. He has published numerous research papers in top-tier

international publications including a number of IEEE transactions on these

topics. He is on the editorial board or served as guest editor for numerousinternational journals, and he has been invited to give talks in various

international conferences. He was the Principal Investigator for several

national or international projects funded by UK EPSRC or EU. He received

his PhD degree in Image/Video Coding Algorithms from the University of

Glamorgan, UK. He is a Senior Member of IEEE.

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Anu Report