A GACS Modeling Approach for MPEG Broadcast Video

Alheraish, A.; Alshebeili, Saleh; Alamri, T.

doi:10.1109/tbc.2004.828364

Cited by 16 publications

(19 citation statements)

References 12 publications

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“…For MPEG-4 single-layer video and MPEG-4 scalable video (which was RD-inefficient) as well as single-layer H.264 video, extensive traffic modeling, see e.g., [16]- [26], and transport mechanism development, see for instance [27]- [31], have been conducted. Similarly, the network transport of H.264 SVC scalable video has begun to attract significant research interest, see for instance [1], [32]- [37].…”

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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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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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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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“…Video traffic models are generally derived from the statistical analysis of video traces and have been widely studied for MPEG-4 and earlier video coding standards, see for instance [26]- [30]. Traffic modeling for H.264 encoded video has only been considered in few studies so far, see e.g., [31]- [36], and is presently an active area of research.…”

Section: Related Workmentioning

confidence: 99%

Video Transport Evaluation With H.264 Video Traces

Seeling

Reisslein

2012

IEEE Commun. Surv. Tutorials

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150

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Abstract-The performance evaluation of video transport mechanisms becomes increasingly important as encoded video accounts for growing portions of the network traffic. Compared to the widely studied MPEG-4 encoded video, the recently adopted H.264 video coding standards include novel mechanisms, such as hierarchical B frame prediction structures and highly efficient quality scalable coding, that have important implications for network transport. This tutorial introduces a trace-based evaluation methodology for the network transport of H.264 encoded video. We first give an overview of H.264 video coding, and then present the trace structures for capturing the characteristics of H.264 encoded video. We give an overview of the typical video traffic and quality characteristics of H.264 encoded video. Finally, we explain how to account for the H.264 specific coding mechanisms, such as hierarchical B frames, in networking studies.

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“…Similarly, the video traffic of these codecs has been extensively studied, see for instance [15]- [19], and they have been used as a basis for the existing studies on video traffic smoothing, as reviewed in Section IV-A, and buffer management, as reviewed in Section V.…”

Section: Related Workmentioning

confidence: 99%

“…Video traces are employed in simulation studies of the transport of video over communication networks, see e.g., [33]- [37], and as a basis for video traffic models, as for instance in [12], [15], [16], [19], [38]- [41]. Traffic modeling of H.264/AVC and H.264 SVC video traffic is a nascent research area, see e.g., [21], [42]- [44], and we directly employ the video traces for a realistic representation of H.264 video traffic in our simulations.…”

Section: B Video Sequencesmentioning

confidence: 99%

Implications of Smoothing on Statistical Multiplexing of H.264/AVC and SVC Video Streams

Auwera

Reisslein

2009

IEEE Trans. on Broadcast.

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Abstract-While the hierarchical B frames based Scalable Video Coding (SVC) extension of the H.264/AVC standard achieves significantly improved compression over the initial H.264/AVC codec, the SVC video traffic is significantly more variable than H.264/AVC traffic. The higher traffic variability of the SVC encoder can lead to smaller numbers of streams supported with bufferless statistical multiplexing than with the H.264/AVC encoder (and even less streams than with the MPEG-4 Part 2 encoder) for prescribed link capacities and loss constraints. In this paper we examine the implications of video traffic smoothing on the numbers of statistically multiplexed H.264 SVC, H.264/AVC, and MPEG-4 Part 2 streams, the bandwidth requirements for streaming, and the introduced delay. We identify the levels of smoothing that ensure that more H.264 SVC streams than H.264/AVC streams can be supported. For a basic low-complexity smoothing technique that is readily applicable to both live and prerecorded streams, we identify the levels of smoothing that give (bufferless) statistical multiplexing performance close to an optimal off-line smoothing technique. We thus characterize the trade-offs between increased smoothing delay and increased statistical multiplexing performance for both H.264/AVC, which employs classical B frames, and H.264 SVC, which employs hierarchical B frames. We similarly identify the buffer sizes for the buffered multiplexing of unsmoothed H.264 SVC, H.264/AVC, and MPEG-4 Part 2 streams that give close to optimal performance.

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A GACS Modeling Approach for MPEG Broadcast Video

Cited by 16 publications

References 12 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

Video Transport Evaluation With H.264 Video Traces

Implications of Smoothing on Statistical Multiplexing of H.264/AVC and SVC Video Streams

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