TITRE Adaptive Packet Video Streaming Over IP Networks - LaBRI

More documents

Recommendations

Info

We run our classification algorithm with the parameters described in the previous section, and we compute the output of each neuron. We choose the largest neuron output which reflects the high similarity. We choose also the value of λ 1 k = λ2k = λ3k = 1 which connect each feature from the vector the network. The result output of the neuron network is shown is Table 4-3. As said, we choose the largest output from the network. According to these results we can see that O1 is marked with AF11 PHB and that O2, O3 are both marked with AF12 PHB. Regarding the execution time, we have measured the mapping of 100 AVO is about 30 millisecond. The complexity of this algorithm is Ο ( n 2 ) . EF AF11 AF12 AF13 Best O 1 3.96 3.98 4.02 0.70 2.60 O 2 3.90 3.98 4.00 0.69 2.60 O 3 3.81 3.90 3.60 0.62 2.49 Table 4-3: Results output of the RBF network 4.1.3.2 Experimental Results Regarding the performance measurements, we will interest on the network parameters such the end-to-end one-way delay encountered by video packet between the server and the destination, and the packet loss probability for each video object stream. This performance metrics are given only for comparison purpose between the scenario with classification (scenario 1) and without classification (scenario 2) model. Figure 4-7 shows the end-to-end video packet transfer delay in both scenarios. We remark that the end-to-end transfer delay increases when the amount of video sent by the server increases (time t=30s). The network load is about 85% (8,5 Mbits). When comparing Figure 4-7 (a) and (b), we note that using the classification and prioritization mechanism (scenario), we get the maximum QoS expected. Important streams (AVOs) will be transmitted as soon as possible to the player and with respect to the QoS required by it. The mean transfer delay is about 114 s in scenario 1 and 120s in scenario 2. Figure 4-8 enforces this measurement by providing loss ratio. We notice that important video objects are protected against loss since they are marker with low drop precedence and this in case of scenario 1. In scenario 2, the loss can affect important object during network congestion (time=30s). This metric, shows a better protection of relevant video objects in the scene during transmission and network congestion. 72
190 180 170 End-to-end delay MPEG-4 Object 3: The person MPEG-4 Object 2: The background MPEG-4 Object 1: The Logo 160 Time (s) 150 140 130 120 110 100 0 200 400 600 800 1000 1200 1400 1600 <strong>Packet</strong>s Number (a) Standard MPEG-4 framework 116 115 End-to-end delay MPEG-4 Object 3: The person MPEG-4 Object 2: The background MPEG-4 Object 1: The Logo 114 Time (ms) 113 112 111 110 109 0 200 400 600 800 1000 1200 1400 1600 <strong>Packet</strong>s Number (b) MPEG-4 framework with Classification Layer Figure 4-7: End-to-end transmission delay 73
Page 1:
UNIVERSITÉ DE VERSAILLES SAINT-QUE
Page 4 and 5:
Résumé Nous constatons aujourd’
Page 6 and 7:
3 Related Work ....................
Page 8 and 9:
5.5.1.2 Simulation Results.........
Page 10 and 11:
List of Figures Figure 1-1: Structu
Page 12 and 13:
Figure 5-15: End-to-end packet tran
Page 14 and 15:
Acknowledgments The first person I
Page 16 and 17:
[11] Toufik Ahmed, Guillaume Burida
Page 18 and 19:
d’accès. Les interactions de QoS
Page 20 and 21:
Métrique MAX_DELAY PREF_MAX_DELAY
Page 22 and 23:
1.1.2 Proposition dun Protocole d'E
Page 24 and 25:
des paquets RTP. Reste toujours le
Page 26 and 27:
Nous pouvons distinguer trois possi
Page 28 and 29:
1.1.2.5.2 Multiplexage des Flux El
Page 30 and 31:
l’horloge est par défaut égale
Page 32 and 33:
La valeur R TCP représente le déb
Page 34 and 35:
Pour ces raisons, nous nous intére
Page 36 and 37:
Telles que illustrés par la Figure
Page 38 and 39: 3. DMIF2SIP vérifie si son propre
Page 40 and 41: utilisée optionnellement pour loca
Page 42 and 43: Chapter 2 2 Introduction 2.1 Motiva
Page 44 and 45: conditions. Based on end-to-end fee
Page 46 and 47: Chapter 3 3 Related Work Nowadays,
Page 48 and 49: • multicasting: it takes the stre
Page 50 and 51: expensive, fixed delivery and recep
Page 52 and 53: 3.1.6 Static vs. Dynamic Channels T
Page 54 and 55: environment because they require a
Page 56 and 57: H.261 is called video codec for aud
Page 58 and 59: VideoScene (VS) VideoObject (VO) Vi
Page 60 and 61: The emphasis of MPEG-7 will be the
Page 62 and 63: Temporal scalability involves parti
Page 64 and 65: multitude of streams. It can switch
Page 66 and 67: increased linearly in the absence o
Page 68 and 69: 3.2.2.1 End-to-End Retransmission R
Page 70 and 71: correctly received block. The missi
Page 72 and 73: epresent user traffic. The delay ca
Page 74 and 75: 3.2.5 IP Signaling Protocols for Pa
Page 76 and 77: two H.323 endpoints or between an e
Page 79 and 80: Chapter 4 4 Adaptive Packet Video T
Page 81 and 82: 4.1.1 Video Classification Model Pr
Page 83 and 84: attributes can refer to the QoS par
Page 85 and 86: In RBF, 2 D = X − is the distance
Page 87: Figure 4-6: Experiment testbed In t
Page 91 and 92: Sync Layer. The SL-packetized strea
Page 93 and 94: conveyed by MIME format parameters
Page 95 and 96: the same value. This timing informa
Page 97 and 98: 4.2.2.1 Fragmentation We propose to
Page 99 and 100: Timestamp: Indicates the sampling i
Page 101 and 102: When G is used as generator matrix,
Page 103 and 104: 4.2.3 Performance Evaluation Intens
Page 105 and 106: 0.3 0.25 our proposal classical app
Page 107 and 108: lost packet at the receiver. Conseq
Page 109 and 110: marking scheme. The server must be
Page 111 and 112: The video server adds audio-visual
Page 113 and 114: Marker (TR3CM) [131] to mark the ba
Page 115 and 116: VO1 VO2 AVO Scenario Audio BL EL1 E
Page 117 and 118: Throughput (Kbits) 3000 2500 2000 1
Page 119 and 120: Throughput (Kbits) 3000 2500 2000 1
Page 121 and 122: 4000 3500 3000 2500 2000 1500 1000
Page 123 and 124: 45 40 Original Quality Received Qua
Page 125 and 126: Chapter 5 5 A Dynamic Packet Video
Page 127 and 128: The TCM algorithm is based on a tok
Page 129 and 130: BEGIN Initialize the classification
Page 131 and 132: User Profile (Token Bucket) Traffic
Page 133 and 134: 5.3.3 QoS Management Algorithm Spec
Page 135 and 136: Figure 5-7: Hierarchical aggregatio
Page 137 and 138: • Filters: to put packets into cl
Page 139 and 140:
mechanism associated with the Drop
Page 141 and 142:
Figure 5-16: Packets loss with IP D
Page 143 and 144:
5.5.2.3 Experimental Results Figure
Page 145 and 146:
1 MPEG-4 Based Layer Stream -AF11 M
Page 147 and 148:
0.2 0.18 MPEG-4 Based Layer Stream
Page 149 and 150:
By using our PBNM tool, we allocate
Page 151 and 152:
Id Condition Indicator Action 1 Net
Page 153 and 154:
The proposed marking algorithm bett
Page 155 and 156:
Chapter 6 6 A SIP/MPEG-4 Multimedia
Page 157 and 158:
performed by many ways like SLA (Se
Page 159 and 160:
6.1.1.4 SIP Communication Model SIP
Page 161 and 162:
• It assign a value (network sess
Page 163 and 164:
6.2.1 SIP2DMIF Subsystem The SIP2DM
Page 165 and 166:
Step 1: The MPEG-4 Terminal passes
Page 167 and 168:
1. Set the result C to the empty se
Page 169 and 170:
TCP/UDP port, our implementation us
Page 171 and 172:
Control & Data Plan Video Rate Cont
Page 173 and 174:
characteristics and relevance of ea
Page 175 and 176:
References [1] Kazaa Media Desktop
Page 177 and 178:
[43] B. Schuster, “Fine granular
Page 179 and 180:
[79] D. Bansal and H. Balakrishnan,
Page 181 and 182:
[115] N. Laoutaris, I. Stavrakakis,
Page 183 and 184:
[154] W. Almesberger, J. H. Salim,
Page 185 and 186:
Appendix A A Appendix A: Overview o
Page 187 and 188:
Media aware Delivery unaware ISO/IE
Page 189 and 190:
A.3.3 Scene Description Streams Sce
Page 191:
This appendix presents structure ex
show all

TITRE Adaptive Packet Video Streaming Over IP Networks - LaBRI

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?