LEADER 03591nam 2200649 450 001 9910827845003321 005 20230707202059.0 010 $a1-118-93108-4 010 $a1-118-93106-8 010 $a1-118-93107-6 035 $a(CKB)3710000000099092 035 $a(EBL)1676665 035 $a(SSID)ssj0001221454 035 $a(PQKBManifestationID)11707126 035 $a(PQKBTitleCode)TC0001221454 035 $a(PQKBWorkID)11186841 035 $a(PQKB)10157582 035 $a(MiAaPQ)EBC1676665 035 $a(Au-PeEL)EBL1676665 035 $a(CaPaEBR)ebr10862649 035 $a(CaONFJC)MIL621897 035 $a(OCoLC)878263104 035 $a(EXLCZ)993710000000099092 100 $a20140507h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aVBR video traffic models /$fSavera Tanwir, Harry Perros 210 1$aLondon ;$aHoboken, New Jersey :$cISTE :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (162 p.) 225 1 $aFocus Series 300 $aDescription based upon print version of record. 311 $a1-84821-636-X 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Contents; Introduction; Chapter 1. Video Coding; 1.1. Video coding; 1.2. Video coding standards; 1.2.1. The MPEG video coding standard; 1.2.2. H.264/MPEG-4 AVC; 1.2.3. H.264 SVC; 1.2.4. H.264 MVC; 1.3. Rate control; 1.4. Summary; Chapter 2. Video Traffic Modeling; 2.1. The AR models; 2.1.1. Review of the AR process; 2.1.2. Survey of AR video traffic models; 2.2. Models based on Markov processes; 2.2.1. Review of Markov process models; 2.2.2. Survey of Markov process models; 2.2.3. Summary; 2.3. Self-similar models; 2.3.1. A survey of self-similar models for video traffic 327 $a3.1.3. A Markov-modulated gamma model3.1.4. A wavelet model; 3.2. Experimental setup; 3.3. Frame size distribution and ACF comparisons; 3.4. QoS evaluation; 3.4.1. End-to-end delay; 3.4.2. Jitter; 3.4.3. Packet loss; 3.4.4. The simulation model; 3.4.5. Results; 3.5. Conclusion; Chapter 4. Evaluation of Video Traffic Model For H.264 MVC Video; 4.1. A video traffic model for MVC video; 4.2. Experimental setup; 4.3. Results; 4.3.1. Q-Q plots and ACF comparisons; 4.3.2. QoS evaluation; 4.4. Conclusion; Conclusion; Appendix; Glossary; Bibliography; Index 330 $aThere has been a phenomenal growth in video applications over the past few years. An accurate traffic model of Variable Bit Rate (VBR) video is necessary for performance evaluation of a network design and for generating synthetic traffic that can be used for benchmarking a network. A large number of models for VBR video traffic have been proposed in the literature for different types of video in the past 20 years. Here, the authors have classified and surveyed these models and have also evaluated the models for H.264 AVC and MVC encoded video and discussed their findings. 410 0$aFocus series (London, England) 606 $aDigital video 606 $aSound$xRecording and reproducing$xDigital techniques 606 $aSystem analysis 615 0$aDigital video. 615 0$aSound$xRecording and reproducing$xDigital techniques. 615 0$aSystem analysis. 676 $a006.696 700 $aTanwir$b Savera$01653905 702 $aPerros$b Harry 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910827845003321 996 $aVBR video traffic models$94005426 997 $aUNINA