Dynamic QoS mapping control for streaming video in relative service differentiation networks

Shin, Jitae; Kim, Jin-Gyeong; Kim, Jong-Won; Kuo, C.-C. Jay

doi:10.1002/ett.4460120309

Cited by 18 publications

(6 citation statements)

References 13 publications

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“…4 shows the error propagation of a lost MB, in which the initial error of the corrupted MB is denoted by u (x, y) and its energy is measured in terms of error variance r 2 u . The propagation error v (x, y) in consecutive frames has energy r 2 v ðm; jÞ in impaired MB j of frame (n + m) [19,20].…”

Section: Derivation Of Mb-level and Gob-level Corruption Modelmentioning

confidence: 99%

A novel content-aware interleaving for wireless video transmission

Choi

Shin

2006

Computer Communications

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Section: Derivation Of Mb-level and Gob-level Corruption Modelmentioning

confidence: 99%

A novel content-aware interleaving for wireless video transmission

Choi

Shin

2006

Computer Communications

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“…Also, assuming that the encoder is familiar with the error concealment method at the decoder, the initial error for the MB can be estimated by differentiating to a general error propagation behavior, where the initial error which propagates to subsequent frames is governed by the effects of the temporal dependency and prediction loop filtering. Under such a scenario and by making use of the results of our previous work [11], we can estimate the total impact of an MB loss in terms of its error energy by…”

Section: Quantitative Packet-priority Metric With Video Content-awarementioning

confidence: 99%

Content-Aware Packet-Level Interleaving Method for Video Transmission over Wireless Networks

Choi

Shin

2005

Lecture Notes in Computer Science

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Abstract. In the wireless network environment, the effect of transmission errors and losses on the video quality varies depending on the intensity of the burst and which parts of the video stream are lost. Among the existing transmission error control techniques, FEC and ARQ are good solutions for combating transmission errors, but they require redundant data. Although interleaving has no error correcting capability, it can improve subjective video quality without wasting additional bandwidth, because it allows the spreading of successive errors. In this paper, we propose a content-aware packet-level interleaving method, which uses a quantitative index to indicate the degree of content-importance of the video content, so that the effect of burst packet losses is distributed intelligently. The proposed scheme improves the overall video quality in comparison with content-blind interleaving methods.

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“…However, the initial framework provided a starting point for many extensions, several of which incorporated support for absolute QoS bounds. Some of them, [13], [14], [15], [16], rely on admission control or adaptive class selection. Others, such as JoBS, proposed in [17], [18], [19], and the scheme of [20], [21] (denoted as PractQoS in the rest of the paper, because of its original characterization as a "practical solution for proportional QoS"), focus on per-hop performances and control the actual level of service directly.…”

Section: Related Workmentioning

confidence: 99%

A simple FIFO-based scheme for differentiated loss guarantees

Huang

Guérin

2007

Computer Networks

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Today's Internet carries an ever broadening range of application traffic with different requirements. This has stressed its original, one-class, best-effort model, and has been one of the main drivers behind the many efforts aimed at introducing QoS. Those efforts have, however, experienced only limited success because their added complexity often conflict with the scalability requirements of the Internet. This has motivated many proposals that try to offer service differentiation while keeping complexity low. This paper shares similar goals and proposes a simple scheme, BoundedRandomDrop (BRD), that supports multiple service classes. BRD focuses on loss differentiation, as although both losses and delay are important performance parameters, the steadily rising speed of Internet links is progressively limiting the impact of delay differentiation. BRD offers strong loss differentiation capabilities with minimal added cost. BRD does not require traffic profiles or admission controls. It guarantees each class losses that, when feasible, are no worse than a specified bound, and enforces differentiation only when required to meet those bounds. In addition, BRD is implemented using a single FIFO queue and a simple random dropping mechanism. The performance of BRD is investigated for a broad range of traffic mixes and shown to consistently achieve its design goals. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Abstract-Today's Internet carries an ever broadening range of application traffic with different requirements. This has stressed its original, one-class, best-effort model, and has been one of the main drivers behind the many efforts aimed at introducing QoS. Those efforts have, however, experienced only limited success because their added complexity often conflict with the scalability requirements of the Internet. This has motivated many proposals that try to offer service differentiation while keeping complexity low. This paper shares similar goals and proposes a simple scheme, BoundedRandomDrop (BRD), that supports multiple service classes. BRD focuses on loss differentiation, as although both losses and delay are important performance parameters, the steadily rising speed of Internet links is progressively limiting the impact of delay differentiation. BRD offers strong loss differentiation capabilities with minimal added cost. BRD does not require traffic profiles or admission controls. It guarantees each class losses that, when feasible, are no worse than a specified bound,...

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Dynamic QoS mapping control for streaming video in relative service differentiation networks

Cited by 18 publications

References 13 publications

A novel content-aware interleaving for wireless video transmission

A novel content-aware interleaving for wireless video transmission

Content-Aware Packet-Level Interleaving Method for Video Transmission over Wireless Networks

A simple FIFO-based scheme for differentiated loss guarantees

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