A traffic for MPEG-coded VBR streams

Krunz, Marwan; Hughes, Herman D.

doi:10.1145/223586.223592

Cited by 52 publications

(16 citation statements)

References 12 publications

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“…Some video encoders adjust the quality of video streams to produce constant bit rate video streams which makes them less complex when being transmitted. But this results in noticeable quality degradation for the video [16,29]. A burst transmission algorithm for constant-bit-rate (CBR) video streams is proposed in [25].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Transmission of Variable-Bit-Rate Video Streams to Mobile Devices

2011

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Section: Related Workmentioning

confidence: 99%

Adaptive Transmission of Variable-Bit-Rate Video Streams to Mobile Devices

2011

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“…Most of these methods characterize the video sources using sophisticated stochastic models, such as Markov models [2], [3], autoregressive [4], self-similar [5], [6], and S-BIND [7], [8] models. These approaches are all stochastic and do not provide a deterministic bound on the traffic.…”

Section: Traffic Characterizationmentioning

confidence: 99%

Data transmission schemes for DVD-like interactive TV

Azimi

Nasiopoulos

Ward

2006

IEEE Trans. Multimedia

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Abstract-Current interactive services for digital TV are limited. They basically display a web page alongside the TV program, which enhances the viewer's experience by providing extra information about the TV program. We define new interactive services for digital TV, which provide DVD-like interactivity to TV viewers. These services enable viewers to control the content and final presentation of a TV program. Some of the attractive applications of our services include parental management, multilingual audio, multiangle video, video in video, etc. The challenge in implementing these services is in transmitting an extra audio or video stream (called incidental) along with the main streams of the TV program. In the first part of this paper, we present a framework for adding the incidental streams to the original transmission stream without increasing the required bandwidth, degrading the picture quality of the main streams, or violating the compatibility of the transmitted stream with standard TV receivers. In the second part of this paper, we explore the two basic mechanisms of the presented framework: traffic characterization and admission control. We present methods for implementing these mechanisms. Using our methods, one can determine whether a TV transmission network has the capability of sending an incidental stream or not. Simulations were conducted to test the validity of our method. The results verify that our method successfully transmits the incidental streams without any discrepancy and without affecting the quality of the main streams.

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“…Let be the new augmented matrix. Thus (31) Then, the matrix can be updated recursively. Let be the updated matrix.…”

Section: ) Determining the Best Column Vector To Addmentioning

confidence: 99%

“…The first category deals with the development of stochastic source models. The paper written by Bae and Suda [4] is a good survey of such video models [6], [23], [28], [31]. These approaches model video source traffic using a variety of methods such as Markov chains, statistical techniques, and correlation functions.…”

Section: Introductionmentioning

confidence: 99%

Sparse Basis Selection: New Results and Application to Adaptive Prediction of Video Source Traffic

Atiya

Aly

Parlos³

2005

IEEE Trans. Neural Netw.

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Abstract-Real-time prediction of video source traffic is an important step in many network management tasks such as dynamic bandwidth allocation and end-to-end quality-of-service (QoS) control strategies. In this paper, an adaptive prediction model for MPEG-coded traffic is developed. A novel technology is used, first developed in the signal processing community, called sparse basis selection. It is based on selecting a small subset of inputs (basis) from among a large dictionary of possible inputs. A new sparse basis selection algorithm is developed that is based on efficiently updating the input selection adaptively. When a new measurement is received, the proposed algorithm updates the selected inputs in a recursive manner. Thus, adaptability is not only in the weight adjustment, but also in the dynamic update of the inputs. The algorithm is applied to the problem of single-step-ahead prediction of MPEG-coded video source traffic, and the developed method achieves improved results, as compared to the published results in the literature. The present analysis indicates that the adaptive feature of the developed algorithm seems to add significant overall value.Index Terms-Internet traffic, MPEG, sparse basis, sparse representation, video traffic prediction.

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A traffic for MPEG-coded VBR streams

Cited by 52 publications

References 12 publications

Adaptive Transmission of Variable-Bit-Rate Video Streams to Mobile Devices

Adaptive Transmission of Variable-Bit-Rate Video Streams to Mobile Devices

Data transmission schemes for DVD-like interactive TV

Sparse Basis Selection: New Results and Application to Adaptive Prediction of Video Source Traffic

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