Comparison of stream merging algorithms for media-on-demand

Bar-Noy, Amotz; Goshi, Justin; Ladner, Richard E.; Tam, Kenneth

doi:10.1007/s00530-003-0114-3

Cited by 34 publications

(41 citation statements)

References 23 publications

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“…[25], five stream merging algorithms for media-ondemand are compared. Using various distributions for receiver request pattern, empirical study results are presented for the dynamic Fibonacci, dyadic and earliest reachable merge target (ERMT).…”

Section: Related Workmentioning

confidence: 99%

“…Then, the existing recovery stream is removed and the acceptable latency of new recovery stream (L ns ) is assigned with the minimum acceptable latency among its receivers (L min:ns ). The conditional statements described from line number (25) to line number (38) correspond to new stream generation and latency assignment of new stream and its receivers.…”

Section: : Nsaddreceiver(i) 29mentioning

confidence: 99%

“…To reduce the number of streams and alleviate network bandwidth usage on the sender, existing stream merging schemes (Refs. [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25]) can be applied. Because these existing schemes assume mainly video-on-demand (VoD) applications, they need to keep the jitter of the data stream delivery as small as possible for smooth video playback.…”

Section: Introductionmentioning

confidence: 99%

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A Lost Data Recovery Scheme for Sensor Data Stream Multicasting

Win

Yoshihisa

Ishi

et al. 2018

Journal of Information Processing

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Abstract:In this paper, we propose a lost data recovery scheme called the synchronized recovery stream merging (SRSM) for sensor data stream multicasting in the unstable networks of the IoT applications. We propose two types of SRSM. The first one is "Latency-aware synchronized recovery stream merging (SRSM-L)" for IoT applications that has restrictions of acceptable latency. The other is "Bandwidth-dependent synchronized recovery stream merging (SRSM-B)" for the cases in which the network bandwidth for the sender is limited. Our proposed schemes reduce the number of the streams managed by the sender by waiting for other recovery streams to synchronize the delivery timing and merging. From our simulation evaluations, we confirmed that our proposed schemes save network bandwidth on the sender in the random and burst failure situations. We confirmed that SRSM-L could reduce the network bandwidth of the sender about 52% in the random failure situation, keeping the acceptable latency. We also confirmed that SRSM-B could keep the specified number of streams constant and the latency overheads small in the burst failure situation.

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Section: Related Workmentioning

confidence: 99%

Section: : Nsaddreceiver(i) 29mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Lost Data Recovery Scheme for Sensor Data Stream Multicasting

Win

Yoshihisa

Ishi

et al. 2018

Journal of Information Processing

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“…Patching methods allow receivers to immediately join an existing multicast session, while the missed portions are transmitted via temporary unicast connections [21,18,7]. It requires that the receiver receives video from both unicast and multicast sessions concurrently and buffers the video from the multicast session [21].…”

Section: Reasons Behind Choosing Lte As An Example Networkmentioning

confidence: 99%

Hybrid multicast-unicast streaming over mobile networks

Rahman

Hsu

Hasib

et al. 2014

2014 IFIP Networking Conference

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Modern cellular networks support unicast and multicast video streaming. Streaming over unicast results in high energy saving for mobile devices, but it can only serve a limited number of users.Whereas multicast streaming can serve large number of mobile users, but it increases the energy consumption for mobile devices. We formulate a resource allocation problem that considers both unicast and multicast simultaneously to maximize the average energy saving across all mobile devices and to serve large number of mobile users. We propose efficient algorithms to solve this problem. Our simulation results show that the proposed algorithms: (i) result in high energy saving close to optimal solutions, (ii) serve large number of mobile users compared to that of unicast-only approach, (iii) are more elastic than unicast-and multicast-only approaches, and (iv) lead to higher energy saving as more network resources are allocated to streaming services.

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“…ERMT performs better than other hierarchical stream merging alternatives and close to the optimal solution, which assumes that all request arrival times are known in advance. 7,15,24 These three stream merging techniques differ in complexity and performance. Patching is the simplest to implement since it allows only one merge during the client's service period and allows only regular streams to be shared.…”

mentioning

confidence: 99%

Efficient cost-based scheduling for scalable media streaming

Sarhan

Qudah

2007

SPIE Proceedings

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The number of media streams that can be supported concurrently is highly constrained by the stringent requirements of real-time playback and high transfer rates. To address this problem, media delivery techniques, such as Batching and Stream Merging, utilize the multicast facility to increase resource sharing. The achieved resource sharing depends greatly on how the waiting requests are scheduled for service. Scheduling has been studied extensively when Batching is applied, but up to our knowledge, it has not been investigated in the context of stream merging techniques, which achieve much better resource sharing. In this study, we analyze scheduling when stream merging is employed and propose a simple, yet highly effective scheduling policy, called Minimum Cost First (MCF). MCF exploits the wide variation in stream lengths by favoring the requests that require the least cost. We present two alternative implementations of MCF: MCF-T and MCF-P. We compare various scheduling policies through extensive simulation and show that MCF achieves significant performance benefits in terms of both the number of requests that can be serviced concurrently and the average waiting time for service.

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Comparison of stream merging algorithms for media-on-demand

Cited by 34 publications

References 23 publications

A Lost Data Recovery Scheme for Sensor Data Stream Multicasting

A Lost Data Recovery Scheme for Sensor Data Stream Multicasting

Hybrid multicast-unicast streaming over mobile networks

Efficient cost-based scheduling for scalable media streaming

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