Perceptual quality-aware active queue management for video transmission

Ghoreishi, Seyed Ehsan; Aghvami, A.H.; Martini, Maria G.

doi:10.1109/pimrc.2015.7343493

Cited by 2 publications

(1 citation statement)

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“…If the current available bandwidth is not enough to transmit the layer i+1, clearly also the layers above will not be sent. Moreover, regarding a multi-stream video transmission scenario, [6] proposes a perceptual quality-aware active queue management, which is designed for scalable video traffic. In order to reduce the queuing delay and queue length, the proposed scheme selectively drops packets from layers that have little influence on video quality, introducing a minimal perceptual quality reduction (PQR) in the stream.…”

Section: Introductionmentioning

confidence: 99%

Buffer management for scalable video streaming

Figueroa¹,

Favalli²

2017

ICST Transactions on Mobile Communications and Applications

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In a video streaming scenario, to cope with bandwidth variations, buffer management and intelligent drop packets policies play a critical role in the final quality of the video received at the user side. In this context, we present a buffer management strategy implemented at the source of a video communication system. This scheme uses priority information from the H.264/SVC encoder, network information from a Bandwidth Estimation approach (BE), based on Hidden Markov Model (HMM) and monitors buffer fullness: when it exceeds a defined threshold, the selective discard strategy takes place. To get more flexibility, we employed SNR quality scalability (Medium Grain Scalability), to get more than one rate point for each enhancement layer. Low priority packets correspond to higher quality layers and are discarded first, with the aim to preserve as much as possible more relevant lower layer packets. Dependencies created by the encoding process are kept into account. We show that the strategy presented ensures that the video transmitted has the highest possible quality under the given network conditions and buffer resources.

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Section: Introductionmentioning

confidence: 99%