An H.261-compatible fuzzy-controlled coder for videophone sequences

Leone, A.; Bellini, A.; Guerrieri, R.

doi:10.1109/fuzzy.1994.343758

“…an application [11] [7] in maintaining a constant video rate by varying the encoder quantization parameter according to the output buffer state, which is a complex control problem without an analytical solution.…”

Section: Related Workmentioning

confidence: 99%

Interval Type-2 Fuzzy Logic Congestion Control for Video Streaming Across IP Networks

Jammeh

¹

,

Fleury

²

,

Wagner

³

et al. 2009

IEEE Trans. Fuzzy Syst.

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Intelligent congestion control is vital in encoded video streaming of a clip or film, as network traffic volatility requires constant adjustment of the bit-rate. Equation-based solutions to congestion control are prone to fluctuations in the delivery rate and may respond only when packet loss has already occurred, while both fluctuations and packet loss affect the end-user's perception of the delivered video. A type-1 fuzzy logic controller can operate at video display rates and can cope with uncertainties in packet delay measurements but this paper proposes an interval type-2 fuzzy logic congestion controller, as this has the ability to anticipate un-modeled network states, besides potentially reducing training time prior to deployment. The paper demonstrates an order of magnitude improvement in delivered video quality using type-2 fuzzy logic in respect to type-1 logic, when the control inputs are subject to noise, and reduced packet loss compared to an equation-based controller.

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“…For the same reason, in a number of papers, the authors of [18] have explored fuzzy logic to improve the performance of the Random Early Discard (RED) router queue algorithm and in [19] DiffServ buffer occupancy for each class of layered video packets. Within video coding fuzzy logic has found an application [11] [7] in maintaining a constant video rate by varying the encoder quantization parameter according to the output buffer state, which is a complex control problem without an analytical solution.…”

Section: Related Workmentioning

confidence: 99%

Interval type-2 fuzzy logic congestion control of video streaming

Jammeh¹,

Fleury²,

Wagner³

et al. 2008

4th International Conference on Intelligent Environments (IE 08)

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Intelligent congestion control is vital in encoded video streaming of a clip or film, as network traffic volatility requires constant adjustment of the bit-rate. Equation-based solutions to congestion control are prone to fluctuations in the delivery rate and may respond only when packet loss has already occurred, while both fluctuations and packet loss affect the end-user's perception of the delivered video. A type-1 fuzzy logic controller can operate at video display rates and can cope with uncertainties in packet delay measurements but this paper proposes an interval type-2 fuzzy logic congestion controller, as this has the ability to anticipate un-modeled network states, besides potentially reducing training time prior to deployment. The paper demonstrates an order of magnitude improvement in delivered video quality using type-2 fuzzy logic in respect to type-1 logic, when the control inputs are subject to noise, and reduced packet loss compared to an equation-based controller.

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“…Two factors imply that a mathematical model is unsuitable: the inputs are dependent on the outputs as there are feedback channels, implying that the problem is nonlinear; and the complexity of multiple constraints is an obstacle. Within video coding, FLC has already found an application [16,17] in maintaining a constant video rate by varying the encoder quantization parameter according to the output buffer state, which is a complex control problem without an analytical solution. Therefore, FLC is a natural candidate for the solution of this problem.…”

Section: Eurasip Journal On Advances In Signal Processingmentioning

confidence: 99%

Power-Constrained Fuzzy Logic Control of Video Streaming over a Wireless Interconnect

Razavi

¹

,

Fleury

²

,

Ghanbari

³

2008

EURASIP J. Adv. Signal Process.

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Wireless communication of video, with Bluetooth as an example, represents a compromise between channel conditions, display and decode deadlines, and energy constraints. This paper proposes fuzzy logic control (FLC) of automatic repeat request (ARQ) as a way of reconciling these factors, with a 40% saving in power in the worst channel conditions from economizing on transmissions when channel errors occur. Whatever the channel conditions are, FLC is shown to outperform the default Bluetooth scheme and an alternative Bluetooth-adaptive ARQ scheme in terms of reduced packet loss and delay, as well as improved video quality.

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An H.261-compatible fuzzy-controlled coder for videophone sequences

Cited by 11 publications

References 9 publications

Interval Type-2 Fuzzy Logic Congestion Control for Video Streaming Across IP Networks

Interval Type-2 Fuzzy Logic Congestion Control for Video Streaming Across IP Networks

Interval type-2 fuzzy logic congestion control of video streaming

Power-Constrained Fuzzy Logic Control of Video Streaming over a Wireless Interconnect

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