Adaptive end-to-end optimization of mobile video streaming using QoS negotiation

Taal, Jacco; Langendoen, Koen; Schaaf, Arjen van der; Dijk, Hylke W. van; Lagendijk, R.L.

doi:10.1109/iscas.2002.1009775

Cited by 5 publications

(3 citation statements)

References 5 publications

(3 reference statements)

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“…On the other hand, using packet loss characteristic modeled with two-state Gilbert model, Bolot, et al [35] proposed the mechanism that determines the degree of FEC together with TFRC. In the presence of packet losses due to channel errors rather than network congestion, FEC techniques and its effectiveness for end-to-end protocols were examined, and the adaptive FEC schemes were proposed to combat such packet losses [19], [20]. For video applications using TCP, Tsugawa, et al [8] applied an adaptive FEC scheme to TCP in order to avoid throughput fluctuations which result in video quality degradation.…”

Section: Related Workmentioning

confidence: 99%

GENEVA: Streaming Control Algorithm Using Generalized Multiplicative-increase/additive-decrease

Matsuzono

Asaeda

Nakamura

et al. 2013

Journal of Information Processing

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Motivated by the deployment of wide-area high-speed networks, we propose GENEVA, the streaming control algorithm using generalized multiplicative-increase/additive-decrease (GMIAD). Because current typical congestion controllers such as a TCP-friendly rate control prevent occurrences of network congestion reacting susceptibly to packet loss, it causes a significant degradation of streaming quality due to low-achieving throughput (i.e., lower throughput than the maximum throughput that a streaming flow requires in maximum audio/video quality) and data packet losses. GENEVA avoids this problem by allowing a streaming flow to maintain moderate network congestion while trying to recover lost data packets that other competing flows cause during the process of probing for available bandwidth. Using the GMIAD mechanism, the FEC window size (the degree of FEC redundancy per unit time) is adjusted to suppress occurrences of bursty packet loss, while trying to effectively utilize network resources that other competing flows cannot consume due to reductions in the transmission rate in response to packet loss. We describe the GENEVA algorithm and evaluate its effectiveness using an NS-2 simulator. The results show that GENEVA enables high-performance streaming flows to retain higher streaming quality under stable conditions while minimizing the adverse impact on competing TCP performance.

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Section: Related Workmentioning

confidence: 99%

GENEVA: Streaming Control Algorithm Using Generalized Multiplicative-increase/additive-decrease

Matsuzono

Asaeda

Nakamura

et al. 2013

Journal of Information Processing

View full text Add to dashboard Cite

show abstract

“…A detailed discussion of ARC falls outside the scope of this paper. We refer to [6,7,10]. (ii) The behavior of the source and channel coders should be modeled parametrically such that joint optimization of the key parameters can take place.…”

Section: Problem Formulationmentioning

confidence: 99%

“…We then measured the overall distortion denoted byD GOP and compared that with the predicted distortion D GOP (using (4), (10), and (15)). Figure 11 shows the results.…”

Section: Optimal Rate Allocationmentioning

confidence: 99%

Error Resilient Video Compression Using Behavior Models

Taal

Chen

et al. 2004

EURASIP J. Adv. Signal Process.

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Wireless and Internet video applications are inherently subjected to bit errors and packet errors, respectively. This is especially so if constraints on the end-to-end compression and transmission latencies are imposed. Therefore, it is necessary to develop methods to optimize the video compression parameters and the rate allocation of these applications that take into account residual channel bit errors. In this paper, we study the behavior of a predictive (interframe) video encoder and model the encoders behavior using only the statistics of the original input data and of the underlying channel prone to bit errors. The resulting data-driven behavior models are then used to carry out group-of-pictures partitioning and to control the rate of the video encoder in such a way that the overall quality of the decoded video with compression and channel errors is optimized.

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Quality of service controlled adaptive video-coding over IEEE 802.11 wireless links

Taal

Haratcherev

Langendoen

et al. 2003

2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698)

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Adaptive end-to-end optimization of mobile video streaming using QoS negotiation

Cited by 5 publications

References 5 publications

GENEVA: Streaming Control Algorithm Using Generalized Multiplicative-increase/additive-decrease

GENEVA: Streaming Control Algorithm Using Generalized Multiplicative-increase/additive-decrease

Error Resilient Video Compression Using Behavior Models

Quality of service controlled adaptive video-coding over IEEE 802.11 wireless links

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