QoE-Based Analysis of DASH Streaming Parameters Over Mobile Broadcast Networks

Lentisco, Carlos M.; Bellido, Luis López; Cuéllar, Q Juan Carlos; Pastor, Encarna; Arciniegas, L H Jose

doi:10.1109/access.2017.2755438

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…In which, Y i [ k ] is the original pixel point, QM is a quantization table,

Y_{{QM}^{i}} [k]

is the corresponding pixel quantization value, the use of “Mi, QM, GT, hi, A0, ht, Qe” in the full text is the definition when it first appeared, after calculating the active factor of the current block, establish a linear relationship between the active factor and the quantization table index Qscale of the block, determine the quantization table index value according to the activity factor of each block, and then determine the quantization step size to achieve dynamic quantification of each macroblock 20 . The linear relationship between active factor Mi and Qscale index is shown in Figure 2 21 . Based on the different spectral characteristics of text information and natural image information, the active factor of the two is not the same, the active factor of text graphic block is generally larger, and the active factor of natural image block is generally small 22 .…”

Section: Methodsmentioning

confidence: 99%

The computer desktop image compression based on clustering algorithm

Gui¹

2020

Concurrency and Computation

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With the rapid development of computer software and hardware technology and network technology, aiming at the limitations of the traditional image compression standards and the deficiencies of the existing computer desktop image compression methods. By analyzing the characteristics of computer desktop image, according to the characteristics of desktop compression, a compression scheme based on high efficiency video coding (HEVC) and color clustering is proposed, which divides blocks into text/graphics blocks, natural image blocks and mixed blocks based on the features of histogram information and texture information of blocks. In block division and classification, an adaptive dynamic block classification algorithm is proposed which is different from the traditional block partitioning. Compared with the traditional method, the new block classification algorithm can save the code stream and improve the classification accuracy.

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“…In which, Y i [ k ] is the original pixel point, QM is a quantization table,

Y_{{QM}^{i}} [k]

Section: Methodsmentioning

confidence: 99%

The computer desktop image compression based on clustering algorithm

Gui¹

2020

Concurrency and Computation

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“…MOS often presents QoE in HAS. Many objective QoE models [8], [12], [13] use metrics such as media bitrate, quality switches, and stall events to predict the MOS for a streaming session algorithmically, and some also include the startup delay [14]. To calculate the perceived or actual MOS, it is recommended to conduct subjective evaluations and seek out the subjects' opinions on a specific streaming session.…”

Section: Introductionmentioning

confidence: 99%

Intense: In-Depth Studies on Stall Events and Quality Switches and Their Impact on the Quality of Experience in HTTP Adaptive Streaming

Taraghi¹,

Nguyen

Amirpour

et al. 2021

IEEE Access

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With the recent growth of multimedia traffic over the Internet and emerging multimedia streaming service providers, improving Quality of Experience (QoE) for HTTP Adaptive Streaming (HAS) becomes more important. Alongside other factors, such as the media quality, HAS relies on the performance of the media player's Adaptive Bitrate (ABR) algorithm to optimize QoE in multimedia streaming sessions. QoE in HAS suffers from weak or unstable internet connections and suboptimal ABR decisions. As a result of imperfect adaptiveness to the characteristics and conditions of the internet connection, stall events and quality level switches could occur and with different durations that negatively affect the QoE. In this paper, we address various identified open issues related to the QoE for HAS, notably (i) the minimum noticeable duration for stall events in HAS; (ii) the correlation between the media quality and the impact of stall events on QoE; (iii) the end-user preference regarding multiple shorter stall events versus a single longer stall event; and (iv) the end-user preference of media quality switches over stall events. Therefore, we have studied these open issues from both objective and subjective evaluation perspectives and presented the correlation between the two types of evaluations. The findings documented in this paper can be used as a baseline for improving ABR algorithms and policies in HAS.

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“…However, they just give a mathematical expression of vMOS based on their subjective estimations. The work of [19] presents using vMOS to investigate the impacts of several system parameters on long-term evolution (LTE) networks based on simulations. To the best of our knowledge, there is no data-driven QoE analysis on vMOS.…”

Section: Introductionmentioning

confidence: 99%

Data analysis on video streaming QoE over mobile networks

Wang

Dai

et al. 2018

J Wireless Com Network

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One of recent proposals on standardizing quality of user experience (QoE) of video streaming over mobile network is video Mean Opinion Score (vMOS), which can model QoE of video streaming in 5 discrete grades. However, there are few studies on quantifying vMOS and investigating the relationship between vMOS and other quality of service (QoS) parameters. In this paper, we address this concern by proposing a novel data analytical framework based on video streaming QoE data. In particular, our analytical model consists of K-means clustering and logistic regression. This model integrates the benefits of both these two models. Moreover, we conduct extensive experiments on realistic dataset and verify the accuracy of our proposed model. The results show that our proposed framework outperforms other existing methods in terms of prediction accuracy. Moreover, our results also show that vMOS is essentially affected by many QoS parameters such as initial buffering latency, stalling ratio, and stalling times. Our results offer a number of insights in improving QoE of video streaming over mobile networks.

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QoE-Based Analysis of DASH Streaming Parameters Over Mobile Broadcast Networks

Cited by 14 publications

References 18 publications

The computer desktop image compression based on clustering algorithm

The computer desktop image compression based on clustering algorithm

Intense: In-Depth Studies on Stall Events and Quality Switches and Their Impact on the Quality of Experience in HTTP Adaptive Streaming

Data analysis on video streaming QoE over mobile networks

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