By Patrick Seeling
Multimedia networking purposes and, specifically, the shipping of c- pressed video are anticipated to give a contribution signi?cantly to the tra?c sooner or later net and instant networks. For shipping over networks, video is sometimes encoded (i. e. , compressed) to lessen the bandwidth standards. Even compressed video, notwithstanding, calls for huge bandwidths of the order of hundred kbps or Mbps. furthermore, compressed video streams in general - hibit hugely variable bit charges (VBR) in addition to lengthy variety dependence (LRD) homes. This, together with the stringent caliber of carrier (QoS) necessities (loss and hold up) of video tra?c, makes the delivery of video tra?covercommunicationnetworksachallengingproblem. Asaconsequence, within the final decade the networking study group has witnessed an - plosion in study on all features of video delivery. The features of video tra?c, video tra?c modeling, in addition to protocols and mechanisms for the e?cient delivery of video streams, have obtained loads of curiosity between networking researchers and community operators and a plethora of video shipping schemes were constructed. For constructing and comparing video delivery mechanisms and for - seek on video networking mostly, it will be significant to have on hand a few characterizationofthevideo. Generally,therearethreedi?erentwaystoch- acterize encoded video for the aim of networking study: (i)video tra?c model,(ii) video bit movement, and (iii) video tra?c trace.
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30 3 Video Encoding Base Layer 36 2 8 −2 1 0 0 0 0 −1 0 0 0 0 0 0 0 0 0 −1 1 −1 1 −4 0 0 2 2 0 −1 1 0 0 0 0 0 0 0 0 0 −1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 −1 −1 0 0 0 0 0 0 1 Enhancement Layer Fig. 15: Data partitioning by priority break point setting. Transcoding is another alternative to scalable encoding. Transcoding can be used to adapt to the diﬀerent network conditions, as in , or to adapt to diﬀerent desired video formats . The transcoding approach requires typically a high performance intermediate node.
N − 1, denote the frame period (display time) of frame n. Let Tn , n = 1, . . , Tn = k=0 tk (and deﬁne T0 = 0). Let Xn , n = 0, . . , N − 1, denote the frame size (number of bit) of the encoded (compressed) video frame frame n. Let QYn , n = 0, . . , N − 1, denote the quality (in terms of the Peak Signal to Noise Ratio (PSNR)) of the luminance component of the encoded (and subsequently decoded) video V frame n (in dB). Similarly, let QU n and Qn , n = 0, . . , N − 1, denote the qualities of the two chrominance components hue (U) and saturation (V) of the encoded video frame n (in dB).
The Hurst parameter, or self– similarity parameter, H, is a key measure of self-similarity [103, 104]. H is a measure of the persistence of a statistical phenomenon and is a measure of the length of the long range dependence of a stochastic process. 5 indicates absence of self-similarity whereas H = 1 indicates the degree of persistence or a present long–range dependence. 4 Periodogram We estimate the Hurst parameter H using the heuristic least squares regression in the spectral domain, see [99, Sec.