An optimal bandwidth allocation strategy for the delivery of compressed prerecorded video

Feng, Wu-chi; Jahanian, Farnam; Sechrest, Stuart

doi:10.1007/s005300050062

Cited by 90 publications

(81 citation statements)

References 13 publications

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“…(그림 2 ) 스무딩 기법의 구조 스무딩 기법을 위한 알고리즘에는 CBA(Critical Bandwidth Allocation) [5,6,7], MCBA(Minimum Critical Bandwidth Allocation) [8], MVBA(Minimum Variability Bandwidth Allocation) [9,10,11], PCRTT(Piecewise Constant Rate Transmission and Transport) [12], e-PCRTT(enhanced--PCRTT) [13] 등이 있다.…”

Section: 서론unclassified

Performance Evaluation focused on Burst of Smoothing Algorithms

Lee

2012

Journal of Digital Contents Society

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The burst is to require abruptly high transmission rate in case of transmitting pre-stored variable bit rate video data, and it causes to be inefficient use of network resource, resource reservation. To avoid these problems, smoothing is transmission plan where variable rate video data is converted to a constant bit rate stream. These smoothing algorithms include CBA, MCBA, MVBA and others.To evaluate amount of burst reduction in the existing CBA, MCBA, MVBA algorithm, this paper compares the burst-related-factors of transmission plan in smoothing algorithms with original video sources which were stored Variable Bit Rate. There are maximum frame bytes, maximum GOP bytes, transmission rate variability per frame, transmission rate variability per GOP in burst-related evaluation factors.Experimental result shows burst-related factors of smoothing algorithms which were used for experiment lower than that of pre-stored video data, except special case.

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Section: 서론unclassified

Performance Evaluation focused on Burst of Smoothing Algorithms

Lee

2012

Journal of Digital Contents Society

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“…One fundamental property of stored video, as mentioned in the Introduction and observed in many other papers [12][13][14][15][16][17][18], is that it is prefetchable. Prefetching is advantageous for at least three reasons.…”

Section: Streaming Stored Videomentioning

confidence: 99%

Optimal streaming of layered video

Cuetos

Ross²

2003

Proceedings of the Eleventh ACM International Conference on Multimedia

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Abstract-This paper presents a model and theory for streaming layered video. We model the bandwidth available to the streaming application as a stochastic process whose statistical characteristics are unknown a priori. The random bandwidth models short term variations due to congestion control (such as TCP-friendly conformance). We suppose that the video has been encoded into a base and an enhancement layer, and that to decode the enhancement layer the base layer has to be available to the client. We make the natural assumption that the client has abundant local storage and attempts to prefetch as much of the video as possible during playback. At any instant of time, starvation or partial starvation can occur at the client in either of the two layers. During periods of starvation, the client applies video error concealment to hide the loss. We study the dynamic allocation of the available bandwidth to the two layers in order to minimize the impact of client starvation. For the case of an infinitely-long video, we find that the optimal policy takes on a surprisingly simple and static form. For finite-length videos, the optimal policy is a simple static policy when the enhancement layer is deemed at least as important as the base layer. When the base layer is more important, we design a threshold policy heuristic which switches between two static policies. We provide numerical results that compare the performance of no-prefetching, static and threshold policies.

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“…Several algorithms have been suggested for computing the schedule of transfer for a given video [3,10,13] (see [5] for a survey). Briefly, these algorithms assume a pair of constraints D(t),B(t) which respectively denote the minimum cumulative data which the server must transmit by time t to prevent underflow and the maximum cumulative data which the server may transmit by time t without overflow.…”

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confidence: 99%

“…The algorithms differ from one another with respect to the criterion they optimize. Feng and Sechrest [6] minimize the number of rate increases, Feng et al [3] the total number of rate changes and McManus and Ross [10] minimize client buffer requirements for a constraint on the total number of CBR transmission segments. In this paper, we base our work on the optimal smoothing algorithm proposed by Salehi et al [13], which produces a schedule that has minimum peak, variance and rate variability.…”

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confidence: 99%

Fast techniques for the optimal smoothing of stored video

Rao

Raghavan

1999

Multimedia Systems

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Abstract. Work-ahead smoothing is a technique whereby a server, transmitting stored compressed video to a client, utilizes client buffer space to reduce the rate variability of the transmitted stream. The technique requires the server to compute a schedule of transfer under the constraints that the client buffer neither overflows nor underflows. Recent work established an optimal off-line algorithm (which minimizes peak, variance and rate variability of the transmitted stream) under the assumptions of fixed client buffer size, known worst case network jitter, and strict playback of the client video. In this paper, we examine the practical considerations of heterogeneous and dynamically variable client buffer sizes, variable worst case network jitter estimates, and client interactivity. These conditions require on-line computation of the optimal transfer schedule. We focus on techniques for reducing on-line computation time. Specifically, (i) we present an algorithm for precomputing and storing the optimal schedules for all possible client buffer sizes in a compact manner; (ii) we show that it is theoretically possible to precompute and store compactly the optimal schedules for all possible estimates of worst case network jitter; (iii) in the context of playback resumption after client interactivity, we show convergence of the recomputed schedule with the original schedule, implying greatly reduced on-line computation time; and (iv) we propose and empirically evaluate an "approximation scheme" that produces a schedule close to optimal but takes much less computation time.

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An optimal bandwidth allocation strategy for the delivery of compressed prerecorded video

Cited by 90 publications

References 13 publications

Performance Evaluation focused on Burst of Smoothing Algorithms

Performance Evaluation focused on Burst of Smoothing Algorithms

Optimal streaming of layered video

Fast techniques for the optimal smoothing of stored video

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