Client-Side QoE Management for SVC Video Streaming: An FSM Supported Design Approach

Kumar, Satish; Devaraj, Rajesh; Sarkar, Arnab; Sur, Arijit

doi:10.1109/tnsm.2019.2926720

Cited by 15 publications

(14 citation statements)

References 23 publications

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“…We assume the video library contains 50 video files, and each video is divided into fixed duration segments. Besides, each video segment is encoded into 4 layers, and the bit rate of each enhancement layer is set as w 0 , w 1 , w 2 , and w 3 separately [37]. All the significant simulation parameters are collected in Table 2.…”

Section: Numerical Experiments and Resultsmentioning

confidence: 99%

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QoE‐Oriented Cooperative Broadcast Optimization for Vehicular Video Streaming

Liu

Feng

Sun

et al. 2021

Wireless Communications and Mobile Computing

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The popularity of online vehicular video has caused enormous information requests in Internet of vehicles (IoV), which brings huge challenges to cellular networks. To alleviate the pressure of base station (BS), Roadside Units (RSUs) and vehicle peers are introduced to collaboratively provide broadcast services to vehicle requesters where vehicles act as both service providers and service requesters. In this paper, we propose an efficient framework leveraging scalable video coding (SVC) technique to improve quality of experience (QoE) from two perspectives: (1) maximizing the data volume received by all requesters and (2) determining buffer action based on playback fluency and average playback quality. For (1), potential providers cooperate to determine the precached video content and delivery policy with the consideration of vehicular mobility and wireless channel status. If one provider fails, other sources will complement to provide requested content delivery. Therefore, their cooperation can improve the QoE and enhance the service reliability. For (2), according to buffer occupancy status, vehicle requesters manage buffer action whether to buffer new segments or upgrade the enhancement level of unplayed segment. Furthermore, the optimization of the data volume is formulated as an integer nonlinear programming (INLP) problem, which can be converted into some linear integer programming subproblems through McCormick envelope method and Lagrange relaxation. Numerical simulation results show that our algorithm is effective in improving total data throughput and QoE.

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Section: Numerical Experiments and Resultsmentioning

confidence: 99%

“…There are some works covering this topic. Kumar et al presented an adaptive QoE management mechanism deciding whether to download a new video segment or smooth the definition level of unplayed video segments [37]. And the proposed strategy can improve delivery satisfactory QoE in highly varying bandwidth state conditions.…”

Section: 3mentioning

confidence: 99%

QoE‐Oriented Cooperative Broadcast Optimization for Vehicular Video Streaming

Liu

Feng

Sun

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…The drawback of this kind of approach is the need for complete communication architecture to control the rendering devices, with obvious challenges regarding scalability and manageability. The main current approach for QoE evaluation is to map the Quality of Service (QoS) to QoE [17], [18] or to derive the QoE from the audio-visual services [19], [20], not taking into consideration any of the additional sensory effects. Another approach is based on the exponential interdependency between QoS and QoE (IQX hypothesis) [21].…”

Section: Introductionmentioning

confidence: 99%

A QoE Model for Mulsemedia TV in a Smart Home Environment

Jalal

Puddu

Martini

et al. 2023

IEEE Trans. on Broadcast.

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The provision to the users of realistic media contents is one of the main goals of future media services. The sense of reality perceived by the user can be enhanced by adding various sensorial effects to the conventional audio-visual content, through the stimulation of the five senses stimulation (sight, hearing, touch, smell and taste), the so-called multi-sensorial media (mulsemedia). To deliver the additional effects within a smart home (SH) environment, custom devices (e.g., air conditioning, lights) providing opportune smart features, are preferred to adhoc devices, often deployed in a specific context such as for example in gaming consoles. In the present study, a prototype for a mulsemedia TV application, implemented in a real smart home scenario, allowed the authors to assess the user's Quality of Experience (QoE) through test measurement campaign. The impact of specific sensory effects (i.e., light, airflow, vibration) on the user experience regarding the enhancement of sense of reality, annoyance, and intensity of the effects was investigated through subjective assessment. The need for multi sensorial QoE models is an important challenge for future research in this field, considering the time and cost of subjective quality assessments. Therefore, based on the subjective assessment results, this paper instantiates and validates a parametric QoE model for multi-sensorial TV in a SH scenario which indicates the relationship between the quality of audiovisual contents and user-perceived QoE for sensory effects applications.

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“…In addition, another strategy based on Scalable Video Coding (SVC) technique to improve the adaptability of HAS is mentioned in [22]. In [22], they took two steps of loading the segment and then smoothing it to increase the quality of the user experience.…”

Section: Introductionmentioning

confidence: 99%

Flexible QoE optimized Video Adaptive Streaming over HTTP for sudden bandwidth drops

Viet¹,

Ngoc²,

Hương³

2022

Preprint

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<p>We have observed a boom in video streaming over the Internet, especially during the Covid-19 pandemic, that could exceed the network resource availability. In addition to upgrading the network infrastructure, finding a way to smartly adapt the streaming system to the network and users’ conditions to satisfy clients’ perceptions is exceptionally critical, too. This paper proposes a new QoE-aware adaptive streaming scheme over HTTP - ABRA - to make flexible adaptations based on the network and the client’s current status. Besides, we propose a technique that can keep the buffer at an average high for more than 10s. We were limiting the phenomena of rebuffering due to unexpected and unpredictable bandwidth changes. The algorithm keeps the quality of subsequent versions at a constant level even when the average bitrate decreases, increasing the QoE. Experimental results show that our method can improve QoE from 7.86% to 20.41% compared to state-of-the-art methods. ABRA can provide better performance in terms of QoE score in all buffer conditions compared to the existing solutions while maintaining a minimum secured buffer level for the worst case. </p>

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Client-Side QoE Management for SVC Video Streaming: An FSM Supported Design Approach

Cited by 15 publications

References 23 publications

QoE‐Oriented Cooperative Broadcast Optimization for Vehicular Video Streaming

QoE‐Oriented Cooperative Broadcast Optimization for Vehicular Video Streaming

A QoE Model for Mulsemedia TV in a Smart Home Environment

Flexible QoE optimized Video Adaptive Streaming over HTTP for sudden bandwidth drops

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