MOS-Based Multiuser Multiapplication Cross-Layer Optimization for Mobile Multimedia Communication

Khan, Shoaib; Duhovnikov, Svetoslav; Steinbach, Eckehard; Kellerer, Wolfgang

doi:10.1155/2007/94918

Cited by 87 publications

(57 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The decrease in video quality caused by transmission errors is expressed 3 YUV test sequences taken from http://media.xiph.org/video/derf/ 4 libavcodec ver. 0.6.2 of FFmpeg, http://www.ffmpeg.org as decrement in peak signal-to-noise ratio (PSNR), which is easier to interpret than mean-squared errors (MSE), since the relationship between perceived subjective quality and PSNR is considered to be roughly linear when values are in the range of 20 to 40 [36]. All presented PSNR values in this paper cover both channel induced distortion as well as encoding distortion and range from 36 dB to 41 dB when no channel errors occur, thus ensuring that averaging over frame PSNRs does not lead to skewed results.…”

Section: B Simulation Setupmentioning

confidence: 99%

Optimizing Selective ARQ for H.264 Live Streaming: A Novel Method for Predicting Loss-Impact in Real Time

Schier

Welzl

2012

IEEE Trans. Multimedia

View full text Add to dashboard Cite

Abstract-This work proposes a quality-oriented, real-time capable prioritization technique for media units of H.264/AVC video streams. The derivation of estimates is based on the analysis of the macroblock partitioning, the spatial extents of temporal dependencies, and the length and strength of prediction chains existing among macroblocks, thus incorporating the expected impact of error propagation. It is demonstrated how the prioritization scheme can be beneficially integrated into live streaming systems which are characterized by tight timing constraints, with the focus on content-aware selective automatic repeat request mechanisms. Additionally, it is shown how potentially limited feedback can be used to adapt the estimation process to leverage prediction preciseness. The approach is compared against existing techniques in terms of practicability and efficiency, and tested under independent and bursty loss conditions in a wired and a wireless test setup. Moreover, the performance is examined when low-latency and constant bitrate video settings are enforced by using x264's novel encoding feature periodic-intra-refresh. Results of both experiments and simulations indicate that the proposed technique outperforms all reference techniques in nearly all test cases, and that the video quality can be further improved by incorporating receiver feedback.Index Terms-End-to-end distortion modeling, error propagation, H.264/AVC, live video streaming, packet loss, selective automatic repeat request (ARQ).

show abstract

Section: B Simulation Setupmentioning

confidence: 99%

Optimizing Selective ARQ for H.264 Live Streaming: A Novel Method for Predicting Loss-Impact in Real Time

Schier

Welzl

2012

IEEE Trans. Multimedia

View full text Add to dashboard Cite

show abstract

“…Relation between MOS and user satisfaction [18]. been used in [21]. Each user tries to optimize, selfishly, the following expected total discounted MOS:…”

Section: Expected Multimedia Quality Per Epochmentioning

confidence: 99%

MOS-Based Congestion Control for Conversational Services in Wireless Environments

Habachi

Schaar³

et al. 2012

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

Abstract-Nowadays, multimedia applications and specifically streaming systems over wireless networks use the TCP transport protocol. Indeed, TCP can deal with practical issues such as firewalls and also deploys built-in retransmissions and congestion control mechanisms. We propose in this paper a Qualitycentric Mean Opinion Score (MOS) based congestion control that determines an optimal congestion window updating policy for multimedia transmission. Unlike the standard congestion control algorithms, our approach defines a new Additive Increase Multiplicative Decrease (AIMD) algorithm given the multimedia application and the transmission characteristics. In order to get the optimal congestion policy in practice, the sender requires complete statistical knowledge of both multimedia traffic and the network environment, which may not be available in wireless systems. Hence, we propose in this paper, a Partially Observable Markov Decision Process (POMDP) framework in order to determine an optimal congestion control policy which maximizes the long term expected Quality of Experience (QoE) of the receiver. Moreover, the computation of an optimal policy is usually time/process consuming and as wireless devices are capacity-limited, we consider optimal solutions based on temporal difference (TD-λ) online learning algorithms. Finally, we do some practical experiments of our algorithm on a Microsoft Lync testbed with unidirectional and bidirectional communications over a wireless network. We observe that for both scenarios, our algorithm improves significantly the QoE compared to standard AIMD congestion control mechanism.

show abstract

“…The utility function for video streaming is a function of the application data rate R [13], U = M OS(R). We assume a simple linear mapping between the MOS and the peak signal-to-noise ratio (PSNR) [14].…”

Section: Application Modelmentioning

confidence: 99%

A Quality-of-Experience driven bidding game for uplink video transmission in next generation mobile networks

Schroeder

Essaili

Steinbach

et al. 2012

2012 19th IEEE International Conference on Image Processing

Self Cite

View full text Add to dashboard Cite

Centralized approaches to solve resource allocation problems for wireless real-time multimedia communications have been intensively studied but require the availability of meta information about the multimedia content and channel information of all users. In this paper, we propose a Quality of Experience (QoE) driven bidding game for de-centralized uplink resource allocation among multiple mobile video producers. Different from previous works, the price per resource unit is defined on a Mean Opinion Score (MOS) scale and users bid for the resources that maximize their own utility function. Simulations in an LTE environment show the benefits of our distributed approach in terms of convergence time and QoE performance, compared to a centralized greedy scheme and a state-of-theart game-theoretic approach.

show abstract

MOS-Based Multiuser Multiapplication Cross-Layer Optimization for Mobile Multimedia Communication

Cited by 87 publications

References 11 publications

Optimizing Selective ARQ for H.264 Live Streaming: A Novel Method for Predicting Loss-Impact in Real Time

Optimizing Selective ARQ for H.264 Live Streaming: A Novel Method for Predicting Loss-Impact in Real Time

MOS-Based Congestion Control for Conversational Services in Wireless Environments

A Quality-of-Experience driven bidding game for uplink video transmission in next generation mobile networks

Contact Info

Product

Resources

About