UNIVERSITY OF CALIFORNIA

Los Angeles

 

Adaptive Multimedia in Wireless IP Networks

 

 

A dissertation submitted by partial satisfaction of the

requirements for the degree Doctor of Philosophy

in Computer Science

 

by

 

Matheos Ioannis Kazantzidis

 

 

2002

@ Copyright by

Matheos Ioannis Kazantzidis

2002

 

 


The dissertation of Matheos Ioannis Kazantzidis is approved.

 

______________________________

Leonard Kleinrock

 

______________________________

Songwu Lu

 

______________________________

Mani Srivastava

 

______________________________

Mario Gerla, Committee chair

 

 

 

 

 

 

University of California, Los Angeles

2002


 

 

 

 

 

 

 

 

 

 

 

 

“I dedicate this thesis to my parents

Ioannis Kazantzidis and Maria Kazantzidi/Agapaki”


TABLE OF CONTENTS

1      Introduction. 1

2      Background and Related Work. 6

2.1        Application Context 6

2.1.1         Adaptability in Multimedia Streaming. 8

2.2        Feedback. 16

2.3        Transport and Congestion Control 18

2.4        Networks. 25

2.5        Performance Evaluation. 26

2.5.1         Platforms. 26

2.5.2         Accuracy of Measurement versus QoS. 27

2.5.3         Metrics. 30

3      Network Technology. 36

3.1        Bluetooth Simulation Model 44

3.1.1         Scatternets and Inter-Piconet Scheduling Simulation. 45

3.2        Bluetooth Scatternet Architecture. 47

3.2.1         Background. 48

3.2.2         Validation. 52

3.2.3         Rendezvous Points Allocation Schemes. 52

3.2.4         Scatternet Model & Results. 54

3.2.5         Conclusion. 63

4      The Network Feedback Architecture. 66

4.1        Propagation of Available bandwidth. 69

4.2        QM-AODV QoS propagation and aggregation support 70

4.3        802.11 network layer available bandwidth support 77

4.3.1         A Network Layer Implementation. 82

4.3.2         Simulation Correctness. 86

4.3.3         Adaptive Multimedia Simulation. 88

4.3.4         Call Admission Simulation. 89

4.3.5         Conclusion. 90

4.4        The Bluetooth Available Bandwidth Support 91

5      The End-to-End RTP based Architecture. 93

5.1        Development of an Adaptive Audio Client/Server 94

5.1.1         Speech Recognition Extensions. 94

5.1.2         Testbed and Environment 98

5.2        Real 802.11 / Wavelan experiments. 100

5.2.1         CSMA Speech Scheme Real Testbed Results. 103

5.2.2         Issues in Payload adaptation. 108

5.2.3         Larger Scale Real Testbed Experiments. 109

5.2.4         Intra-Protocol Fairness. 112

5.2.5         Tcp and Udp Experiments with hybrid simulator 121

5.2.6         Conclusions. 123

5.3        Adaptive Multimedia in Bluetooth Piconets. 124

5.3.1         Video and TCP. 128

5.3.2         Voice. 129

5.3.3         Adaptive Video and TCP. 131

5.3.4         Conclusion. 132

5.4        Bluetooth Scatternet End-to-End Adaptation. 133

5.4.1         Results. 135

5.5        Ad-hoc Bluetooth and 802.11 Comparison. 139

5.5.1         Conclusions. 145

6      Available Bandwidth. 147

6.1        Packet Pair Background. 147

6.2        Extension of Packet Pair/Train For Available Bandwidth Sampling. 150

6.2.1         Why do we need a new method to calculate the samples?. 150

6.2.2         The ab-probe model 151

6.2.3         The “bytes over time” model 153

6.2.4         Observability and robustness of ab-probe. 155

6.3        Experiments. 158

6.3.1         Active measurement 160

6.3.2         Passive Measurement 165

6.3.3         Wireless Link Measurement 168

6.4        MMTP. 170

6.4.1         Filtering. 171

6.4.2         Confidence based Weighting Algorithm.. 172

6.4.3         Stability and TCP Friendliness. 173

7      Comparisons. 176

7.1        802.11 single hop. 178

7.1.1         QoS. 179

7.2        802.11 multi-hop. 182

7.2.1         QoS. 184

7.2.2         Mobility. 187

7.3        Bluetooth Scatternets. 190

7.3.1         QoS. 192

7.4        TCP Friendliness. 193

8      Conclusion. 196

 


LIST OF FIGURES

Figure 2.1. The two functions of multimedia adaptation. 8

Figure 2.2 Application Context 9

Figure 2.3: General Server Architecture. 15

Figure 2.4:General Client Architecture. 16

Figure 2.5 Mean of Accuracy Normal(mean, 0.1) versus QoS. 30

Figure 2.6. Accuracy Variance versus QoS. 30

Figure 2.7 QoS MPQM related evaluation model used. 35

Figure 3.1 A scatternet with one inter-piconet unit that divides its time between the two piconets. 44

Figure 3.2 Scatternet Simulator and Interface with NS and GlomoSim Bluetooth model 47

Figure 3.3 The number of nodes active in the polling cycle during the superframe cycle of a piconet with s non-gateway slaves and 3 gateway nodes. 51

Figure 3.4 The topology for the validation of the equation. 54

Figure 3.5 Average connectivity degree versus PG (x axis) and GP (y axis) limits. 58

Figure 3.6 Round Robin f.t. without gateway priority, RV-maxmin and RV-Separation : Minimum averaged F.T. against GP (x axis) and PG (y axis) limit 58

Figure 3.7 Round Robin f.t. without gateway priority, RV-maxmin, RV-greedy, RV-tree : Minimum averaged F.T. against GP (x axis) and PG (y axis) limits. 58

Figure 3.8 . Round Robin f.t. without gateway priority, RV-maxmin and RV-Separation : Averaged F.T. against GP (x axis) and PG (y axis) limits. 59

Figure 3.9 . Round Robin f.t. without gateway priority, RV-maxmin, RV-greedy and RV-tree: Average F.T. against GP (x axis) and PG (y axis) limits. 59

Figure 3.10 Piconet pair F.t. distribution for 123 piconets (2,3) (GP,PG). 60

Figure 3.11 . Piconet pair F.t. distribution for 123 piconets (3,3) (GP,PG). 60

Figure 3.12 Piconet pair F.t. distribution for 123 piconets and no imposed limits - (7,7) (GP,PG). 61

Figure 3.13 Hop distance averaged over all piconet pairs in scatternet. (x axis is the GP limit) 62

Figure 3.14 Hop distance distribution for the (3,3) (GP,PG) limit (x axis is hop distance, y axis is number of piconet pairs that are connected with the hop distance with a min-hop route) 62

Figure 3.15 End-to-end route throughput averaged over all piconet pairs for the RV-maxmin case. (x axis is the GP limit, y axis is the route mimimum f.t. ) 63

Figure 3.16 Route throughput distribution for the (3,3) (GP,PG) limit (x axis is route throughput in bps, y axis is an increasing number that represents a piconet pair, it is ordered by route throughput) . 63

Figure 4.1 Node block diagram architecture for network feedback support. 68

Figure 4.2 The network feedback case: accurate measurement and better propagation. 69

Figure 4.3 A scenario of AODV QoS value aggregation and propagation. 77

Figure 4.4 (top) a CBR source uses different packet. (mid) packet delays (bottom) the throughput observed per packet. 79

Figure 4.5 Under low load the throughput measurement becomes independent of packet size by substracting a constant c  80

Figure 4.6 Window operation. 81

Figure 4.7 The 802.11 measurement block diagram.. 82

Figure 4.8 An example packet arrival/ acknowledgement sequence. The network calculates the contributed delays by using the notification series to emulute the link queue. 85

Figure 4.9 Exact measurements for CBR.. 87

Figure 4.10 The VBR source rates in time averaged over 32 packets. 87

Figure 4.11 Accurate measurements for VBR traffic. 87

Figure 4.12 Overall loss rates (%)  per experiment 89

Figure 4.13 Total bytes sent vs total bytes received with call acceptance and without. 89

Figure 5.1 The topology used in the experiments. Three subnets are used to create a multihop connection. Two subnetworks are used for the server and the client respectively. One is used for the interference. The gateway has virtual interfaces to all subnets. 98

Figure 5.2 : Loss Rate when no adaptation is performed, and with standard experiment interference. Audio stream is packetized in 960 byte packets and sampled at 22000 samples per second and 8 bit per sample. Interference is ‘ping’ packets of 40 bytes attempting to fill the channel 103

Figure 5.3 Visualization of text synthesizer use: switching between audio (even number) and text stream (odd number) with different TTS-thresholds in first experiment 104

Figure 5.4 : Loss rates when adapting to QoS, and with standard experiment interference. Audio stream is packetized in 240 byte packets and sampled at 8000 samples per second and 8 bit per sample (due to the adaptation) . Interference is ‘ping’ packets of 40 bytes. 106

Figure 5.5 Visualization of text synthesizer use: switching between audio (even number) and text stream (odd number) with different TTS-thresholds insecond experiment 106

Figure 5.6 : Loss rates when adapting to QoS with extremely adverse network conditions. Audio stream is packetized in 240 byte packets and sampled at 8000 samples per second and 8 bit per sample. Interference is ‘ping’ packets of 160 bytes attempting to fill the channel. The distance between the stations here is larger and the larger propagation delays result in higher loss rates. 107

Figure 5.7 Visualization of text synthesizer use: switching between audio (even number) and text stream (odd number) with different TTS-thresholds in third experiment 107

Figure 5.8 Topology in Large Scale Real Experiments. 110

Figure 5.9 Loss Rates in Clients with FTP interfering traffic. 110

Figure 5.10 Loss Rates in Non-Adaptive Clients with FTP interfering traffic. 110

Figure 5.11 Loss Rates in Clients with only non-TCP intefering traffic. 111

Figure 5.12 A single hop link ping delay graph. 112

Figure 5.13 Adaptation mechanism.. 115

Figure 5.14Averaged client loss rates vs adaptivity. 117

Figure 5.15 Averaged effective bandwidth vs adaptivity. 118

Figure 5.16 Averaged server consumed bandwidth vs adaptivity. 118

Figure 5.17 Averaged Coefficient of Variations in Effective Client Bandwidth. 119

Figure 5.18 Coefficient of Variation of Effective Bandwidth among clients (y axis) for 100 generated topologies (x axis) when TCP/FTP traffic exists. 121

Figure 5.19 Coefficient of variation of client rates of the 10 connections (y axis) for all 100 generated topologies (x axis) with 802.11 MAC and no mobility. 122

Figure 5.20 Coefficient of variation of client rates of the 10 connections (y axis) for all 100 generated topologies (x axis) with 802.11 MAC and mobility. 122

Figure 5.21  Coefficient of variation of client rates of the 10 connections (y axis) for all 100 generated topologies (x axis) with CSMA MAC and no mobility. 123

Figure 5.22 Coefficient of variation of client rates of the 10 connections (y axis) for all 100 generated topologies (x axis) with CSMA MAC and mobility. 123

Figure 5.23 A few seconds from the H263 source trace (sec, bytes) 126

Figure 5.24  Bluetooth end to end adaptation. 128

Figure 5.25  H.263 Non adaptive video and TCP connections aggregate throughput. 129

Figure 5.26 Loss Rates for video connections for H.263. 129

Figure 5.27 Voice Delay Distribution for WaveLAN.. 130

Figure 5.28  Voice Delay Distribution for Bluetooth. 130

Figure 5.29  H.263 aggregate server sent rates. 130

Figure 5.30 Loss Rates for Adaptive H.263 Video. 131

Figure 5.31 H.263 adaptive video and TCP connections aggregate throughput. 132

Figure 5.32 The canonical recursive Bluetooth scatternet topology. 135

Figure 5.33 Per Connection Server Send Throughput and Goodput for Bluetooth and 802.11, adaptive and non-adaptive for the 4 by 4 piconet case. 137

Figure 5.34 Loss rates. 137

Figure 5.35 Jitter and delays for a typical connection. Left: from the 3 by 3 piconets case, Right: From the single piconet case. 137

Figure 5.36 Adaptive connections in the (top) 4 by 4, (middle) 3 by 3 and (bottom) 2 by 2case. Left column shows (top) reported RTP loss rates (middle) layer from which frame is picked for transmission (bottom) received rate averaged over 1 second. Right column shows jitter and delay. 142

Figure 5.37 The corresponding 802.11/DCF exact topology cases (top, left) 64 node, eq. to 4 by 4 (top,right) 36 node, eq. to 3 by 3 piconets (bottom, left)  16 node, eq. to 2 by 2 and (bottom, right) 4 nodes, eq. to 1 piconet Bluetooth case. 143

Figure 5.38 Direct Comparison of Bluetooth and 802.11 MAC using an end-to-end adaptation mechanism. (top) Average Recv throughput, Sent Throughput (middle) Loss rates,  Delay Jitter (bottom) Delay, Packets Delivered  144

Figure 6.1 Possible events in links before the bottleneck link. 147

Figure 6.2 Possible events at the bottleneck link. 148

Figure 6.3 Events occurring after bottleneck link. 148

Figure 6.4 ab-probe model 149

Figure 6.5 “bytes over time” available bandwidth calculation varies with train size while network conditions remain constant. 154

Figure 6.6 The “bytes over time” relative error for a 4Mbps link with 4Mbps up to 1.280Mpbs available  155

Figure 6.7  The “bytes over time” relative error for a 4Mbps link with 1.230Mbps to 100Kbps available  155

Figure 6.8 How an error in Pb estimation affects the available bandwidth sampled with ab-probe. 157

Figure 6.9 Difference between “bytes over time” and ab-probe measurement. 157

Figure 6.10  The short range campus Internet  experiment topology. 159

Figure 6.11 The long range campus Internet  experiment topology. 159

Figure 6.12 The sender bandwidth of the MPEG-4 video. 160

Figure 6.13 Active Measurement using BOT and AB-probe in the short range Internet topology. 163

Figure 6.14 Active Measurement using BOT and AB-probe in the long range Internet topology. 163

Figure 6.15 . Graphs from the short distance active measurement experiment a. Ab-probe short distance packet pair samples when injected traffic is 0Mbps b. BOT packet pair samples when injected cross traffic is 0Mbps c. Ab-probe short distance packet pair samples w when injected cross traffic is 3Mbps e. Ab-probe packet pair samples filtered f. BOT packet pair samples filtered. The measurement is not affected by the injected traffic in all cases. 164

Figure 6.16 Graphs from the long distance active measurement experiment. a. Ab-probe short distance packet pair samples when injected traffic is 0Mbps b. BOT packet pair samples when injected cross traffic is 0Mbps c. Ab-probe short distance packet pair samples when injected cross traffic is 3Mbps e. Ab-probe packet pair samples filtered f. BOT packet pair samples filtered. The measurement is not affected by the injected traffic in all cases. 165

Figure 6.17 Model diversion graph for pairs and trains for the short distance case. 167

Figure 6.18 Model diversion graph for pairs and trains for the long distance case. 167

Figure 6.19 Graphs from the short distance active measurement experiment. a. Ab-probe short distance packet pair samples when injected traffic is 0Mbps b. BOT packet pair samples when injected cross traffic is 0Mbps c. Ab-probe short distance packet pair samples when injected cross traffic is 3Mbps d. BOT packet pair samples when injected cross traffic is 3Mbps (note that the samples may look more than the ones in b. but in reality they are just more scattered) 168

Figure 6.20 Graphs from the wireless measurement experiment. a. Sender timestamps of FTP traffic when injected traffic is 0Mbps b. Sender timestamps of FTP traffic when injected traffic is 3Mbps  c. Ab-probe packet pair samples when injected cross traffic is 0Mbps d. BOT packet pair samples when injected cross traffic is 0Mbps e. Ab-probe packet pair samples when injected cross traffic is 3Mbps f. BOT packet pair samples when injected cross traffic is 3Mbps  168

Figure 6.21 Wireless link measurement 169

Figure 7.1 Topology used in the comparison experiments. M denotes a node that would be a Master in the Bluetooth scenario, S a slave. A direct line between a Slave and a Master indicates a Slave’s Home Piconet, a dotted one that the slave acts as a gateway to another piconet 177

Figure 7.2 Loss Rates. 179

Figure 7.3. QoS, 802.11 single hop. 180

Figure 7.4 Loss Rates, 802.11 single hop, higher rates. 181

Figure 7.5. QoS, 802.11 single hop, loss rates. 182

Figure 7.6 Loss rates on 802.11 multihop. 184

Figure 7.7 QoS on 802.11 multi-hop. 185

Figure 7.8 Loss rates in 802.11 multihop with higher rate video. 186

Figure 7.9 QoS in 802.11 multihop with higher rate video. 187

Figure 7.10 5km/h mobility. 188

Figure 7.11 20km/h mobility. 188

Figure 7.12 55km/h mobility. 189

Figure 7.13. QoS in 5 km/h mobility. 189

Figure 7.14 QoS in 20 km/h mobility. 190

Figure 7.15 QoS in 55km/h mobility. 190

Figure 7.16 Bluetooth Scatternets Loss Rates. 192

Figure 7.17 Bluetooth QoS. 193

Figure 7.18 TCP Friendliness Experiment 195

Figure 8.1 Middleware and Agent techniques expected operation. 199

 


LIST OF TABLES

Table 4‑1 Pseudo code for QM-AODV modifications to AODV.. 75

Table 4‑2 Sample Run of QM-AODV operation. 76

Table 5‑1 Real Testbed parameters. 100

Table 5‑2 Adaptation mechanism parameter values. 115

Table 5‑3 Configurations tested. 115

Table 5‑4 Simulation Parameters. 117

 

 


VITA

November 12, 1971                 Born, Athens, Greece

 

1995                                        Diploma of Higher Education

                                                University of Patras,

                                               Patras, Greece

 

1995                                        Computer Engineer

                                                Advanced Informatics Ltd

                                                Patras, Greece

 

1996-1998                               Teaching Assistant

                                                University of California

Department of Computer Science

                                                Los Angeles, California

 

1998                                        Recipient of The Gerondelis Foundation Fellowship

 

1998                                        M.S. Computer Science

                                                University of California

                                                Los Angeles, California

 

1998-2002                               Research Assistant

                                                University of California

Department of Computer Science

                                                Los Angeles, California

 

2002                                                                                Recipient of the Fred W. Ellersick Prize

Formerly known as

The Communications Society Magazine Prize Paper Award

 

 

 

PUBLICATIONS AND PRESENTATIONS

 

Kazantzidis M., Chen T., Romanenko Y., Gerla M. (March, 1999) An Ultimate Encoding Layer for Layered Real-Time Speech Streams over Multi-hop Wireless Networks. Proceedings of IEEE 2nd Annual Conference on Wireless Comm., San Diego, CA

 

Kazantzidis M., Tang K., Gerla M. (May, 1999) Validation of Multi-Layer Simulation Experiments via Analysis and Measurements. Proceedings of DARPA/NIST Network Simulation Workshop, Fairfax, VA

 

Kazantzidis M., Slain I., Chen T., Romanenko Y., Gerla M. (June, 1999) Experiments on QoS Adaptation for Improving End user Speech Perception over Multihop Wireless Networks. Proceedings of IEEE ICC, Vancouver, Canada.

 

Kazantzidis M., Wang L., Gerla M. (November, 1999) On Fairness and Efficiency of Adaptive Audio Application Layers for Multihop Wireless Networks. Proceedings of IEEE MOMUC'99, San Diego, CA

 

Kazantzidis M. (1999) Increasing Speech Perception in face of external interference  - Secretary of the Army Louis Caldera UCLA visit

 

Gerla M., Kazantzidis M., Pei G., Talucci F., and Tang K. (2000) Ad Hoc, Wireless, Mobile Networks: The Role of Performace Modeling and Evaluation.  Book Chapter In Performance Evaluation: Origins and Directions, pp. 51-95, Edited by G. Haring, C. Lindemann, and M. Reiser, Springer-Verlag, 2000.

 

Kazantzidis M. (September 2000) Adaptive Video over Multi-Hop Wireless Networks using Hybrid Simulation – Demonstration Presentation at Digivations 2000, Digital Media Innovation Program, Santa Barbara CA

 

Kazantzidis M. (February 2001) Wireless Adaptive Multimedia using Network Measurements. UCLA Computer Science Technical Report #200102

 

Kazantzidis M., Lee S.J., Gerla M. (2001) Permissible Throughput Network Feedback for Adaptive Multimedia in AODV MANETs – Proceedings of IEEE ICC 2001

 

Kazantzidis M. (2001) How to measure available bandwidth on the Internet. UCLA Computer Science Technical Report #010032

 

Kazantzidis M. (2001) Locally optimal Bluetooth Scatternet formation. UCLA Computer Science Technical Report #010033

 

Kazantzidis M. (2001) End-to-end versus Explicit Feedback Measurement in 802.11 Networks. UCLA Computer Science Technical Report #010034

 

Gerla M., Kapoor R., Kazantzidis M., Johansson P. (2001) Ad hoc Networking with Bluetooth. Wireless Mobile Internet Conference during MobiCom 2001

 

Johansson P., Kapoor R., Kazantzidis M., Gerla M. (October 2001) Bluetooth an Enabler of Personal Area Networking.  IEEE Network Special Issue on Personal Area Networks, Sept-Oct 2001

 

Kazantzidis M., Zanella A., Gerla M. (2002) End-to-end Adaptive Multimedia over Bluetooth Scatternets Proceesings of Eurel AICA European Wireless Conference 2002

 

Johansson P., Kapoor R., Kazantzidis M., Gerla M. (2002) Rendezvous Scheduling in Bluetooth Scatternets – Proceedings of IEEE ICC 2002

 

Johansson P., Kapoor R., Kazantzidis M., Gerla M. (2002) Personal Area Networks: Bluetooth or IEEE 802.11? International Journal of Wireless Information Networks SPECIAL ISSUE ON MOBILE AD HOC NETWORKS (MANETs) Standards, Research, Applications, April 2002

 

Kazantzidis M., Gerla M. (2002) End-to-end versus Explicit Feedback Measurement in 802.11 Networks. Proceedings of the 7th IEEE Symposium on Computers and Communications

 

Kazantzidis M., Gerla M. (2002) On the Impact of Inter-Piconet Scheduling in Bluetooth Scatternets – Proceedings of WWIC 2002


ABSTRACT OF THE DISSERTATION

 

Adaptive Multimedia in Wireless IP Networks

 

by

 

Matheos Ioannis Kazantzidis

Doctor of Philosophy in Computer Science

University of California, Los Angeles, 2002

Professor Mario Gerla, Chair

 

 

Support for video and audio applications is important to single and multi hop wireless networks whether they are used as extensions to the Internet or not. Due to the variability of response of the air medium and the mobility support that is expected of these networks, it is accepted that such applications must dynamically adapt to network conditions, taking advantage of the different content representations achieved by advances in coding. This adaptability targets at maximizing the overall QoS delivered by the network and may be classified into (i) The transport functionality that decides the network parameters e.g. sending rate and (ii) The presentation functionality that decides the content that should fit the network parameters. In wireless, it is particularly difficult to implement an accurate monitoring process (measurement) and embed it into a distributed strategy that efficiently controls the scarce network resources. Therefore, transport protocols designed for wired networks fail. This, combined with scalability challenges of some ad hoc environments (e.g. battlefield) motivates the exploration of an important trade-off for this environment. On the one hand, adopting a thin and scalable network architecture allows for end-to-end adaptation which is limited in measurement accuracy and consequently performance. On the other hand, the implementation of lower layer feedback support leads to architectures that are less scalable and bear a higher deployment cost.  But, can deal effectively with the measurement inaccuracy problem. In order to explore this tradeoff, we study, develop and improve existing end-to-end as well as network feedback strategies in terms of the overall QoS delivered to the network users. We trade-off the end-to-end techniques versus the network feedback techniques and provide a performance gain model that can guide the design of real time applications as well as the design of the networks to support them.

 


1     Introduction

 

A great deal of work is targeted at exploiting adaptive mechanisms in all design layers of wireless networks. The goal is to gain the desired protocol responsiveness that deals with the frequent unexpected changes in grades of service. The air medium and the mobility support expected of both last hop wireless internet and ad-hoc multi-hop wireless networks requires careful and specialized higher layer protocols for congestion control and QoS support. The ones developed for wired networks fail when put to work in a wireless environment.

The responsibility for flow control on the Internet is mainly left at the transport layer, allowing for a scalable design and a thin network layer. The transport peers perform some type of monitoring to its packets and apply sampling and estimation techniques to calculate desirable quantities e.g. trip times, path available bandwidth etc. Explicit help from lower layers is not allowed, as this would impair scalability, make deployment difficult and dramatically increase cost. TCP for example, is using a flow’s single packet loss as an indication of network congestion, presuming that the packet is dropped due some stressed buffer along its path. This however does not work in wireless networks, as packet losses due to external and internal interference are also frequent.

We do not directly deal with TCP in this thesis, as it has been shown to be an unsuitable protocol for multimedia communication, but we frequently refer to it and consider part of the work applicable for TCP protocols. Even if reliability (ARQ and re-ordering) functionality is removed from TCP, it still does not present a good candidate. The trial-and-error approach converges slowly and requires many attempts. On the other hand, multimedia traffic prefers an approach that would, from scratch, operate on a fairly good estimate of the available bandwidth, incurs minimum perceptually costly attempts to improve quality, and adjusts smoother to network changes. Furthermore, window based techniques impose unnecessary delays and high jitter. Real time traffic should ideally be serviced (transmitted) as soon as generated by the application, or packets may not reach the destination by their playback time. Live applications are particularly intolerant of delays, especially transport delays that are always in the critical path.

Adaptive multimedia transports would ideally prefer to have accurate knowledge of the bandwidth available along their path, averaged over a small interval. Let us define the available bandwidth over one link as the link bandwidth minus the used bandwidth, i.e. the un-utilized bandwidth. The path’s available bandwidth then would be the minimum available bandwidth across all links in the end-to-end path. With this information at hand, the peers would be able to adjust their rate so as to minimize their lost, not played packets, perform congestion control and be TCP fair or friendly. At the same time it is hard to measure available bandwidth, especially in an end-to-end fashion. It is a highly variable quantity and constrained to an end-to-end observation, as the Internet Protocol scalable architecture dictates. Current available bandwidth techniques have been developed for wired networks, and have to approximate the network as performing weighted fair queuing on its flows [Pax97]. The Internet, however, cannot distinguish flows, may employ a variety of queuing disciplines and currently has pre-dominantly FIFO routers and therefore current measurements are highly unreliable.

In a nutshell, current end-to-end transport solutions for multimedia communication are largely heuristic and allow significant room for improvement. The same, put to work in wireless networks, are unexplored and non-promising. Their variant response and heterogeneity places even more stringent requirements in sampling, filtering methods and convergence times. Therefore, besides special development of end-to-end methods another option becomes particularly worth exploring, i.e. deploying network support for transports and applications. Let us call such architectures, network feedback architectures.

Such architectures require special node support, possibly in both hardware and software. Each node measures its bandwidth and delay performance. This can be done fairly accurately because lower layers perform it, each knowing their own mechanisms. The values are then propagated using routing or other similar protocols. Eventually they reach the end-hosts where they may be used by transports, applications or measurement based call admission algorithms. Such a setting has the advantage that it may overcome the aforementioned difficulties improving the overall performance and QoS. However, since each node requires special support, deploy-ability, scalability, inter-operability and consequently cost are impaired. Note that per flow QoS is not required, just a per link QoS information estimation and a per routing table entry aggregate variable per QoS metric. Given the bad performance of existing end-to-end techniques and that those requirements vary in wireless networks, it is particularly worth exploring such architectures.

In particular this work deals with the wireless technology to be used for the deployment of personal area and multi-hop networks in ad-hoc as well as Internet extension settings. Namely PANs and MANETs of Bluetooth and 802.11. In Bluetooth both Piconet and Scatternet configurations are of interest. In 802.11 we look at multi-hop and single hop configurations. These two types of wireless networks have a very different philosophy in their medium access control. Bluetooth organizes the nodes into centrally controlled groups called Piconets while 802.11/DCF assume a totally distributed access control so that mobility support is more flexible. These two approaches provide the two basic MAC options over which we explore the measurement accuracy and application adaptation.

We find that existing methods of end-to-end adaptation reduce the application loss rates according to the offered load. This means that it is possible to have QoS improvement when using these techniques over multi-hop wireless networks. However, using present state CODEC technology and rates the above condition is finally true only for small networks and low number of adaptive connections.

We improve end-to-end techniques by using an improved packet dispersion technique that is based on an innovative and intuitive sampling method, other than the ‘bytes-over-time’ which has been extensively used in the past. We show that this method works better than other end-to-end methods. While it pushes the end-to-end limits on network size and number of flows, it is still limited by the round-trip delayed feedbacks, multi-hop measurement noise and reverse path problems.

We therefore develop the network feedback solutions for these networks. Comprised of a per source-destination single hop highly accurate node measurement and a QoS value propagation technique that gets the necessary information available to the sources, it deals very effectively with the congestion control problem of wireless networks. It consistently exhibits increased QoS, using perceptual QoS metrics, when compared to non-adaptive transmission.

In summary, we consider the significant contributions of this work to be:

-                           The study of the limitations of end-to-end techniques using a perceptual QoS evaluation model and hybrid simulation

-                           The development of an asynchronous hybrid simulation platform for Video over large multi-hop networks.

-                           The development of network feedback architectures, measurement and measurement propagation techniques, that effectively deal with the congestion problem maximizing perceptual QoS

-                           The contribution of practically performing locally optimal Inter-Piconet Scheduling in Bluetooth Scatternets.

-                           The simple, low cost, Bluetooth available bandwidth measurement

-                           The 802.11 available bandwidth measurement using the link ACK/LF messages

-                           The Q-AODV extension to support propagation of QoS values in parallel to the QoS routing function.

-                           The AB-probe, and end-to-end available bandwidth measurement that is based on an innovative sampling of packet dispersion and can be used in non-WFQ networks

 


2     Background and Related Work

 

2.1  Application Context

 

Adaptation starts at the application’s flexibility to carry on its useful task, for example meaningful communication in a multimedia conference, in different grades of service. The network is assumed able to support the application’s highest demand at light load. One or more encoders may define the different grades of service by using different compression rates. If the application or middleware is able to switch between layers at any time, extra information needs to be maintained, either encapsulated in an RTP-type [Rtp96] packet or in a separate packet (even stream) indexed or synchronized to the data packet (or stream). This is because the application needs to be aware of the received stream characteristics. This required extra information introduces overhead. In an end-to-end architecture the applications ability to switch between different rates has to be combined with monitoring and quantifying the underlying network conditions. In a network feedback architecture these are provided by the network. Since the codec belongs to the application layer a significant part of the adaptation has to belong there, according to the RTP paradigm.

Multimedia applications are sensitive to lost packets, delayed packets and jitter.  RTP defines how loss and jitter should be estimated. Their monitoring is performed along the end-to-end path and a feedback packet informs the server periodically. The server uses this past interval to adjust its future sending rate. In essence, the underlying assumption is that the near future network response is anticipated to be similar to that experienced in the near past. QoS information is therefore very time-sensitive. Another realization related to the feedback path delay is that, sadly, when we need the QoS information the most –that is when the network conditions are highly adverse- it is exactly when they are usually received late, errored or lost altogether (proportional to how symmetric are the links).

By application context we refer to an ILP architecture that implements at least an RTP thin transport layer, a presentation layer and an application layer suitable for energy efficient, mobile clients. The target applications for this work can be categorized as follows:

·         According to liveliness

Live vs Playback - A source may transmit a stream as it is captured in a live session. Or otherwise a pre-recorded, pre-encoded stored stream is played back from secondary storage in a playback session. The main distinction between live and playback session is that in a live session the future data is not available whereas in a playback all the future data is available at the beginning of the session. Thus the stream can be transmitted arbitrarily faster (or slower) than its consumption rate. Data is still slightly delayed and buffered at the source before transmission to allow for a small transmission rate differences. In both cases data has to be buffered at the client side in order to compensate for the jitter in delivery times. Clearly a live session places more stringent requirements in the buffer size used because use of larger buffers limits interactivity due to the proportionally larger playback delays. Smoothing is therefore limited too in live applications. Adaptive encoding approaches can be used to smooth the bandwidth requirements of the encoding stream, for example [NgK97]

·         According to content:

Audio coding / Video coding - Audio codecs usually require less bandwidth than video. Video codecs employ compression in two levels. On an image level and on a frame level. Image compression is employed to encode the image of one frame. In order to compress further usually a video codec will transmit one compressed full significant frame and a few of the next frames will be encoded only as differences from the significant frame. This implies that, an audio application can usually transmit at a constant average bit rate in a much smaller time scale than a video source. It also implies that in video codecs packets do not have a uniform QoS significance. Furthermore, most audio codecs have been designed for synchronous channels, for example the GSM codec at 13Kbps. In/tolerance  to lost packets is an important attribute to audio codecs. Object coding attempts to encode audio and video (M-peg 4) by objects. A speech application is a constant bit rate with silence, on/off intervals. The Brady model is widely used for voice traffic modeling. A hypermedia application is one that combines audio, video with text and images. In this dissertation we extensively deal with Mpeg audio and video (H.263).

By introducing adaptivity to the application layer, demands placed on the network can be adjusted within the session, and user perception may be enhanced. Application models that support adaptivity have been proposed in [McI98], [Cha97], [Sis97]. An application model suitable for wireless ad-hoc networks can be found in [Kaz99]. Architectural considerations are found in [Cla90], [Ott98], [Boc96].

 

2.1.1        Adaptability in Multimedia Streaming

 

Figure 2.1. The two functions of multimedia adaptation

                                                                                                               

 

            We conceptually classify the functions of multimedia adaptation in two categories: (i) the network transport operations i.e. the network monitor that implements the measurements and congestion control mechanisms and (ii) the presentation operations that constrained by the network resources as reported from (i) direct the coding operations. The challenge in (i) is to perform efficient, stable, distributed and accurate congestion control while the challenge in (ii) is to trade-off quality and error control for the particular content and codec capabilities.

            Live and playback multimedia applications are likely to be developed over any one of the network configuration under study, independently of whether these will provide quality of service or not. An example is the Internet, where shortly after the modems reached the minimum possible bandwidth a significant number of solutions were instantly provided and deployed. Adaptive streaming over IP networks has been the focus of research for this reason.

            In this section we introduce an application architecture framework that will allow us to more effectively study the application context functions.

·         Session Initiation: This has special importance for adaptive multimedia application protocols, since it may include QoS negotiation and call admission as well as initial application buffering. We include both initial buffering and re-buffering in the session initiation since it is common that many protocol parameters are decided during this phase.

Figure 2.2 Application Context