Content-sensitive video streaming over low bitrate and lossy wireless network

Tan, Kun; Ribier, Richard; Liou, Shih-Ping

doi:10.1145/500141.500227

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…To apply these algorithms for real-time streaming, we shall perform piecewise optimization over a limited window looking into the immediate future for (local) sub-optimal decisions. This is still better than other heuristic-based, greed decision rules, which provides no optimality claim [5].…”

Section: Discussion and Concluding Remarksmentioning

confidence: 88%

“…We could not emphasize enough that the idealistic "no-idle assumption" on the server side as imposed at the beginning might not hold in a practical scenario where the bandwidth is unpredictable and can fluctuate significantly. The streaming protocol [5] shall support real time communications between the client and the server to halt transmission/display momentarily whenever buffer failure is to occur. The merit of the proposed algorithm is in that if the channel bandwidth behaves as predicted, the communication overhead between the server and the client will be kept at minimum, or ideally, none.…”

Section: Time-varying Bandwidthmentioning

confidence: 99%

“…In other words, over a low-bit-rate channel, we stream a sequence of nonlinearly sampled frames ("key-frames" or "key-segments") for synchronized display with the audio [5].…”

mentioning

confidence: 99%

“…The issue of content-sensitive video streaming has recently been discussed in the literature in, e.g., [5], [8]- [10]. Although utilizing various video content analysis tools, they only proposed greedy, short-term traffic prediction schemes: [5], [9] applied ad hoc strategies to perform dynamic resource allocation and to increase link utilization; while [8] used a neural network to predict the resource request based on training data that take into account contents and bandwidth statistics.…”

mentioning

confidence: 99%

“…Although utilizing various video content analysis tools, they only proposed greedy, short-term traffic prediction schemes: [5], [9] applied ad hoc strategies to perform dynamic resource allocation and to increase link utilization; while [8] used a neural network to predict the resource request based on training data that take into account contents and bandwidth statistics. None of above provided a global optimal solution-here, "global" can be interpreted as either the span of the full video or a segment across multiple shots/scenes, depending upon the available computational power at the streaming server.…”

mentioning

confidence: 99%

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Optimal nonlinear sampling for video streaming at low bit rates

Zhou

Liou

2002

IEEE Trans. Circuits Syst. Video Technol.

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Over low-bit-rate channels, we adopt the streaming of nonlinearly sampled video frames (i.e., key-frame slideshow) synchronized with the audio stream. Given the channel and buffer limits, we wish to obtain a set of sampled frames that is not only feasible (i.e., streamable), but also optimal in terms of maximal information flow (given that the semantic information contents of each frame can be quantified either automatically or manually). Different application scenarios are considered and modeled in a principle way, for which we propose computationally efficient algorithms for finding the global optimal solution. The contributions of this paper include the novel modeling schemes for channel and buffer limits in the video temporal sampling problem, the analysis and development of the corresponding efficient algorithms for finding the global optimal solution, and the extension and analysis of these algorithms for practical application scenarios. The proposed algorithms have made possible the automated production of the new form of video streaming over low bit rate channels for devices with limited storage capabilities.Index Terms-Dynamic programming, low bit-rate communication, nonlinear temporal sampling, T-C knapsack problem, video streaming, Z-B diagram.

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Section: Discussion and Concluding Remarksmentioning

confidence: 88%

Section: Time-varying Bandwidthmentioning

confidence: 99%

“…In other words, over a low-bit-rate channel, we stream a sequence of nonlinearly sampled frames ("key-frames" or "key-segments") for synchronized display with the audio [5].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Optimal nonlinear sampling for video streaming at low bit rates

Zhou

Liou

2002

IEEE Trans. Circuits Syst. Video Technol.

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Adaptive filtering of MPEG system streams in IP networks

Hemy

Steenkiste

Groß

2006

Multimed Tools Appl

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Congestion and large differences in available link bandwidth create challenges for the design of applications that want to deliver high quality video over the Internet. We present an efficient adaptive filter for MPEG System streams that can be placed in the network (e.g., as an active service). This filter adjusts the bandwidth demands of an MPEG System stream to the available bandwidth without transcoding while maintaining synchronization between the streams embedded in the MPEG System. The filter is network-friendly: it is fair with respect to other (TCP) competing streams and it avoids generating bursty traffic. This paper presents the system architecture and an evaluation of our implementation in three different operating environments: a networking testbed in a laboratory environment, a homeuser scenario (DSL line with 640 Kbit/s), and a wide area network covering the Atlantic (server in Europe, client in the US). Moreover we examine the networkfriendliness of the adaptation protocol and the relationship between the quality of the received continuous media and the protocol's aggressiveness. Our architecture is based on efficient MPEG System filtering to achieve high-quality video over besteffort networks.

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Content-adaptive wireless streaming of instructional videos

Liu

Choudary

2006

Multimed Tools Appl

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Content-sensitive video streaming over low bitrate and lossy wireless network

Cited by 7 publications

References 3 publications

Optimal nonlinear sampling for video streaming at low bit rates

Optimal nonlinear sampling for video streaming at low bit rates

Adaptive filtering of MPEG system streams in IP networks

Content-adaptive wireless streaming of instructional videos

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