Optimal quality adaptation for MPEG-4 fine-grained scalable video

Kim, T.; Ammar, Mostafa

doi:10.1109/infcom.2003.1208714

Cited by 37 publications

(18 citation statements)

References 23 publications

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“…We have seen proposals that make efficient use of FGS coding for adaptive video streaming [11], [28], but they did not consider proxy caching. To our knowledge, [26] is the only work that employed FGS videos in caching.…”

Section: B Fgs Videomentioning

confidence: 99%

Fine-Grained Scalable Video Caching for Heterogeneous Clients

Liu

Xu²,

Chu³

2006

IEEE Trans. Multimedia

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Abstract-Much research has focused on caching adaptive videos to improve system performance for heterogeneous clients with diverse access bandwidths. However, existing rate-adaptive caching systems, which are based on layered coding or transcoding, often suffer from a coarse adaptation and/or a high computation overhead. In this paper, we propose an innovative rate-adaptive caching framework that enables low-cost and fine-grained adaptation by using MPEG-4 fine-grained scalable videos. The proposed framework is both network-aware and media-adaptive; i.e., the clients can be of heterogeneous streaming rates, and the backbone bandwidth consumption can be adaptively controlled. We develop efficient cache management schemes to determine the best contents to cache and the optimal streaming rate to each client under the framework. We demonstrate via simulations that, compared to nonadaptive caching, the proposed framework with the optimal cache management not only achieves a significant reduction in the data transmission cost, but also enables a flexible utility assignment for the heterogeneous clients. Our results also show that the framework maintains a low computational overhead, which implies that it is practically deployable.

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Section: B Fgs Videomentioning

confidence: 99%

Fine-Grained Scalable Video Caching for Heterogeneous Clients

Liu

Xu²,

Chu³

2006

IEEE Trans. Multimedia

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“…This implies that they share real-time requirements with applications like multimedia and video services [44,46,21,16]. Figure 1 shows a prototypical, distributed collaborative visualization [1,48].…”

Section: Real-time Collaborationmentioning

confidence: 99%

IQ‐Services: network‐aware middleware for interactive large‐data applications

Cai¹,

Eisenhauer²,

He³

et al. 2005

Concurrency and Computation

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IQ-Services are application-specific, resource-aware code modules executed by data transport middleware. They constitute a 'thin' layer between application components and the underlying computational and communication resources that implements the data manipulations necessary to permit wide-area collaborations to proceed smoothly, despite dynamic resource variations. IQ-Services interact with the application and resource layers via dynamic performance attributes, and end-to-end implementations of such attributes also permit clients to interact with data providers. Joint middleware/resource and provider/consumer interactions implement a cooperative approach to data management for the large-data applications targeted by our research. Experimental results in this paper demonstrate substantial performance improvements attained by coordinating network-level with service-level adaptations of the data being transported and by permitting end users to dynamically deploy and use application-specific services for manipulating data in ways suitable for their current needs.

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“…1. Several works in the literature consider the problem of resource optimization in multicast streaming of media content, see for instance [4], [5], [6], [7], [8], [9], [10], [11], [12], [13]. There are essentially two main directions of research that are relevant to the topic considered in this paper, both of which are often referred to with the common name of rate allocation, albeit with different meanings.…”

Section: Introductionmentioning

confidence: 99%

Optimal Rate Allocation for P2P Video Streaming

Lima

Dalai

Leonardi

et al. 2013

IEEE J. Select. Areas Commun.

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Abstract-Rate control for multimedia streaming has been the subject of many recent research activities and is crucial for peerto-peer networks, where controlling the rate at the source is ineffective. In this paper we propose a two-stage procedure for rate control. In particular, the first stage, based on Integer Linear Programming and a distortion model, optimally labels packets with a suitable priority level. This stage can be performed only once at the encoder. In a second stage, we derive an optimal strategy to choose prioritized packets for transmission, according to the available rate. This stage can be implemented at the transport level and autonomously by each peer. The techniques are specialized and used to control the rate of a standard H.264/SVC stream. We show by means of experiments that the proposed approach outperforms uncontrolled transmission, and that the proposed optimal priority selection gives substantial advantages over other simplified procedures.Index Terms-Peer-to-peer streaming, congestion control, H.264/AVC, scalable video coding.

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Optimal quality adaptation for MPEG-4 fine-grained scalable video

Cited by 37 publications

References 23 publications

Fine-Grained Scalable Video Caching for Heterogeneous Clients

Fine-Grained Scalable Video Caching for Heterogeneous Clients

IQ‐Services: network‐aware middleware for interactive large‐data applications

Optimal Rate Allocation for P2P Video Streaming

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