Markovian Characterisation of H.264/SVC Scalable Video

Fiems, Dieter; Inghelbrecht, Veronique; Steyaert, Bart; Bruneel, Herwig

doi:10.1007/978-3-540-68982-9_1

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…Similarly, the network transport of H.264 SVC scalable video has begun to attract significant research interest, see for instance [1], [32]- [37]. A traffic model for H.264 SVC temporal scalability of the base layer and the complete enhancement layer has been proposed in [38]. Furthermore, an RD model of H.264 SVC quality scalability through dropping complete enhancement layers has been studied in [39], [40].…”

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confidence: 99%

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H.264 Coarse Grain Scalable (CGS) and Medium Grain Scalable (MGS) Encoded Video: A Trace Based Traffic and Quality Evaluation

Gupta

Pulipaka

Seeling

et al. 2012

IEEE Trans. on Broadcast.

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Abstract-The scalable video coding (SVC) extension of the H.264/AVC video coding standard provides two mechanisms, namely coarse grain scalability (CGS) and medium grain scalability (MGS), for quality scalable video encoding, which varies the fidelity (signal-to-noise ratio) of the encoded video stream. As H.264/AVC and its SVC extension are expected to become widely adopted for the network transport of video, it is important to thoroughly study their network traffic characteristics, including the bit rate variability. In this paper, we report on a large-scale study of the rate-distortion (RD) and rate variability-distortion (VD) characteristics of CGS and MGS. We found that CGS achieves low bit rate overheads in the 10-30% range compared to H.264 SVC single-layer encodings only for encodings with a total of up to three quality levels; more quality levels result in substantially higher overheads. The traffic variabilities of CGS are generally lower than for single-layer streams. We found that in the low to mid range of the MGS quality scalability, MGS can achieve the same or even slightly higher RD efficiency than corresponding single-layer encoding; toward the upper end of the MGS quality scalability range the RD efficiency drops off significantly. MGS layer extraction following the hierarchical B frame structure gives nearly as high RD performance as RD-optimized extraction. In the range of high RD efficiency, MGS streams have significantly higher traffic variabilities than single-layer streams at the frame time scale. At the group-of-pictures (GoP) time scale, MGS has similar or lower levels of traffic variability compared to single-layer streams. Generally, MGS layer extraction over the time horizon of individual GoPs gives significantly lower traffic variability than extraction over the time horizon of the full video sequence.

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confidence: 99%

“…Furthermore, an RD model of H.264 SVC quality scalability through dropping complete enhancement layers has been studied in [39], [40]. Similar to [15], the studies [38]- [40] did not consider medium grain scalability through partitioning of the complete enhancement layer.…”

mentioning

confidence: 99%

H.264 Coarse Grain Scalable (CGS) and Medium Grain Scalable (MGS) Encoded Video: A Trace Based Traffic and Quality Evaluation

Gupta

Pulipaka

Seeling

et al. 2012

IEEE Trans. on Broadcast.

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“…Dai et al [1] have developed a unified traffic model, in which they have tried to fully exploit the crosslayer correlation between base-layer and multiple enhancement layers. Fiems et al [19] have proposed a multivariate Markovian traffic model to characterize H.264/SVC scalable video traces. More recently multiview video traffic modeling has been explored [20].…”

Section: Related Work:video Traffic Model Classificationmentioning

confidence: 99%

Traffic models for H.264 video using hierarchical prediction structures

Pulipaka

Seeling

Reisslein

2012

2012 IEEE Global Communications Conference (GLOBECOM)

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Abstract-We present different video traffic models for H.264 variable bit rate (VBR) videos. We propose our models on top of the recent unified traffic model developed by Dai et al. [1], which presents a frame-level hybrid framework for modeling MPEG-4 and H.264 multi-layer VBR video traffic. We exploit the hierarchical predication structure inherent in H.264 for intraGoP (group of pictures) analysis. We model the children frames by considering various combinations of the correlation between the parent frames in the prediction structure. Our simulations show that modeling using the hierarchical prediction structure indeed improves capturing the statistical features of the videos and prediction of network performance, without an increase in the complexity as compared to the unified traffic model by Dai et al. [1], which was shown earlier to be better than previous traffic models.

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“…[16]. The equilibrium distribution of the phases is found as the 1× probability vector satisfying A(1) = and 1 = 1, where 1 is a ×1 column vector with all entries equal to 1.…”

Section: Introductionmentioning

confidence: 99%

Delay versus energy consumption of the IEEE 802.16e sleep-mode mechanism

Vuyst

Turck

Fiems

et al. 2009

IEEE Trans. Wireless Commun.

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Abstract-We propose a discrete-time queueing model for the evaluation of the IEEE 802.16e sleep-mode mechanism of Power Saving Class (PSC) I in wireless access networks. Contrary to previous studies, we model the downlink traffic by means of a Discrete Batch Markov Arrival Process (D-BMAP) with phases, which allows to take traffic correlation into account. The tradeoff between energy saving and increased packet delay is discussed. In many situations, the sleep-mode performance improves for heavily correlated traffic. Also, when compared to other strategies, the exponential sleep-period update strategy of PSC I may not always be the best.Index Terms-Discrete-time queueing model, sleep mode, IEEE 802.16e, analytic study.

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Markovian Characterisation of H.264/SVC Scalable Video

Cited by 7 publications

References 15 publications

H.264 Coarse Grain Scalable (CGS) and Medium Grain Scalable (MGS) Encoded Video: A Trace Based Traffic and Quality Evaluation

H.264 Coarse Grain Scalable (CGS) and Medium Grain Scalable (MGS) Encoded Video: A Trace Based Traffic and Quality Evaluation

Traffic models for H.264 video using hierarchical prediction structures

Delay versus energy consumption of the IEEE 802.16e sleep-mode mechanism

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