Scheduling video programs in near video-on-demand systems

Abram-Profeta, E.L.; Shin, Kang G.

doi:10.1145/266180.266387

Cited by 25 publications

(15 citation statements)

References 11 publications

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“…• Quasi Video-on-Demand (QVoD): QVoD [15] is a threshold-based NVoD. The server delivers a video when the number of user requests lager than a predefined threshold.…”

Section: Video On Demand Systems and Techniquesmentioning

confidence: 99%

Channel Allocation for Smooth Video Delivery over Cognitive Radio Networks

Luan

Shen

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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Video applications normally demand stringent quality-of-service (QoS) for the high quality and smooth video playback at the receiver. Since the network is usually shared by multiple applications with diverse QoS requirements, QoS provisioning is an important and difficult task for the efficient and smooth video delivery. In the context of cognitive radio (CR) networks, as the secondary or unlicensed users share a pool of bandwidth that is temporarily being unused by the primary or licensed users, there is an inevitable interference between the licensed primary users and the unlicensed CR devices. As a result, efficient and smooth video delivery becomes even more challenging as the channel spectrum is not only a precious resource, but also much more dynamic and intermittently available to secondary users.In this thesis, we focus on the provision of guaranteed QoS to video streaming subscribers in CR network. In video streaming applications, a playout buffer is typically deployed at the receiver to deal with the impact of the network dynamics. With different buffer storage, users can have different tolerance to the network dynamics. We exploit this feature for channel allocation in CR network. To this end, we model the channel availability as an on-off process which is stochastically known. Based on the bandwidth capacity and the specific buffer storage of users, we intelligently allocate the channels to maximize the overall network throughput while providing users with the smooth video playback, which is formulated as an optimization framework. Given the channel conditions and the video packet storage in the playout buffer, we propose a centralized scheme for provisioning the superior video service to users. Simulation results demonstrate that by exploiting the playout buffer of users, the proposed channel allocation scheme is robust against intense network dynamics and provides users with the elongated smooth video playback.

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“…• Quasi Video-on-Demand (QVoD): QVoD [15] is a threshold-based NVoD. The server delivers a video when the number of user requests lager than a predefined threshold.…”

Section: Video On Demand Systems and Techniquesmentioning

confidence: 99%

Channel Allocation for Smooth Video Delivery over Cognitive Radio Networks

Luan

Shen

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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“…True Video-on-Demand systems [3] allow users to have total control: they can pause, rewind, fast forward the stream whenever they like. Such activities put additional strain on the provider's resources; to prevent that, near Video-onDemand [4] systems allow such user actions but at discrete time intervals.…”

Section: Multimedia Streamingmentioning

confidence: 99%

A Case for Multimedia Streaming over the Grid Infrastructure

Lambrinos

Georgatos

2008

Management of Converged Multimedia Networks and Services

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Abstract. The grid infrastructure consists of nodes all over the world which are usually interconnected with high speed network links and provide storage and processing facilities. In this paper, we investigate how this massive infrastructure can be utilized to facilitate efficient and scaleable real-time multimedia streaming. The aim is to avoid the issues usually associated with one-to-many media streaming architectures through the use of a mechanism that initiates reflectors as and when they are needed thereby reducing bandwidth-related bottlenecks and ensuring that the delay between the last media distribution point and the receiving client is as low as possible.

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“…These free channels can be added to the pool of unicast channels to further reduce latency. Define as the latency function for UVoD with channel folding, and then it can be computed from (16) which accounts for the additional unicast channels made available by channel folding. We can also conclude that channel folding is effective only if or .…”

Section: A Latencymentioning

confidence: 99%

Channel folding - an algorithm to improve efficiency of multicast video-on-demand systems

Lee

2005

IEEE Trans. Multimedia

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Abstract-Recently a number of researchers have proposed and investigated new video-on-demand architectures that make use of network multicast to achieve vastly improved efficiency. Techniques such as batching, patching, periodic broadcasting, chaining, and piggybacking have been explored both in isolation and in combinations. This study investigates a new tool in the arsenal-channel folding, where aggressive client-side caching is used to merge clients from one multicast channel into the other. In particular, this channel folding algorithm is applied to a previously proposed unified video-on-demand (UVoD) architecture to demonstrate and to quantify its impact on the performance and the tradeoff in a multicast video distribution architecture. This paper presents this channel folding algorithm in the context of UVoD and derives a performance model to obtain the system latency, the near-optimal channel partition policy, and the client buffer requirement. Numerical results show that channel folding can double the capacity of UVoD with a remarkably small overhead in the client buffer requirement.Index Terms-Caching, channel folding, merging, multicast, performance analysis, unified video-on-demand (UVoD), video-on-demand (VoD).

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Scheduling video programs in near video-on-demand systems

Cited by 25 publications

References 11 publications

Channel Allocation for Smooth Video Delivery over Cognitive Radio Networks

Channel Allocation for Smooth Video Delivery over Cognitive Radio Networks

A Case for Multimedia Streaming over the Grid Infrastructure

Channel folding - an algorithm to improve efficiency of multicast video-on-demand systems

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