Understanding User Experience of Mobile Video: Framework, Measurement, and Optimization

Song, Wei; Tjondronegoro, Dian; Docherty, Michael

doi:10.5772/39053

Cited by 16 publications

(6 citation statements)

References 99 publications

(89 reference statements)

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“…The architecture allows the study and understanding of cross-contextual effects, the assessment of the relative importance of parameters, and the development of a basic algorithmic QoE model. Although Song et al [60] have not proposed exact formulae for QoE calculation, they have organized QoE IFs into three components: user, system, and context and mapped their impacts upon four elements of the mobile video delivery framework, namely, mobile user, mobile device, mobile network, and mobile video service, since the model is created for a mobile video environment. User-centered design of mobile video may benefit from this model, as well as mobile video vendors that may develop effective strategies to improve user's experience.…”

Section: A Survey Of General Qoe Modeling Approachesmentioning

confidence: 99%

“…Based on the comparison given in Table 1, it may be summarized that the majority of discussed approaches [10,20,28,[57][58][59][60], differing in considered type of service and wireless aspects, address the human IFs at both low-level (i.e., physical, emotional, and mental constitution of the user) and high-level processing (i.e., cognitive ability, interpretation, or judgment). These models also consider system IFs classified into content, media, network, and device factors, as well as context IFs, while a set of them such as [55,56] do not explicitly address IFs in that fashion.…”

Section: A Survey Of General Qoe Modeling Approachesmentioning

confidence: 99%

“…In the context of QoE management, all addressed QoE modeling approaches, each in its own way, contribute to this process and may be applied in various contexts such as system or service/application optimization, as well as network resource allocation improvement. Thus several of them have been built to aid monitoring, measurement, and estimation of QoE [20,54,58,59], while others contribute to QoE improvement by diagnosing the main reasons for quality deterioration [55], enabling development of strategies [60], and providing detailed taxonomy [56,57] and causal relationships [10], as well as QoE prediction mechanisms [28].…”

Section: A Survey Of General Qoe Modeling Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Survey and Challenges of QoE Management Issues in Wireless Networks

Baraković

Skorin‐Kapov

2013

Journal of Computer Networks and Communications

121

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With the move towards converged all-IP wireless network environments, managing end-user Quality of Experience (QoE) poses a challenging task, aimed at meeting high user expectations and requirements regarding reliable and cost-effective communication, access to any service, anytime and anywhere, and across multiple operator domains. In this paper, we give a survey of state-of-the-art research activities addressing the field of QoE management, focusing in particular on the domain of wireless networks and addressing three management aspects: QoE modeling, monitoring and measurement, and adaptation and optimization. Furthermore, we identify and discuss the key aspects and challenges that need to be considered when conducting research in this area.

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Section: A Survey Of General Qoe Modeling Approachesmentioning

confidence: 99%

Section: A Survey Of General Qoe Modeling Approachesmentioning

confidence: 99%

Section: A Survey Of General Qoe Modeling Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Survey and Challenges of QoE Management Issues in Wireless Networks

Baraković

Skorin‐Kapov

2013

Journal of Computer Networks and Communications

121

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“…Not surprisingly, Quality of Experience (QoE) metrics which focus on user perception of and satisfaction with service performance have received increased attention over the past several years [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Delivery quality score model for Internet video

Yeganeh

Kordasiewicz

Gallant

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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The vast majority of today's internet video services are consumed over-the-top (OTT) via reliable streaming (HTTP via TCP), where the primary noticeable delivery-related impairments are startup delay and stalling. In this paper we introduce an objective model called the delivery quality score (DQS) model, to predict user's QoE in the presence of such impairments. We describe a large subjective study that we carried out to tune and validate this model. Our experiments demonstrate that the DQS model correlates highly with the subjective data and that it outperforms other emerging models.

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“…In contrast to these works, our study focuses on providing an understanding of user mobility and geographical migrations when using mobile video services. A QoE modeling framework with user, system and context components is created for a mobile video environment, taking mobile user, mobile device, mobile network and mobile video service into consideration [18]. Thus, users requesting mobile videos may benefit from the model, and video providers could also develop effective strategies to improve the user experience.…”

Section: B User Mobility Behaviorsmentioning

confidence: 99%

Understanding Performance of Edge Content Caching for Mobile Video Streaming

Wang

Zhang

et al. 2017

IEEE J. Select. Areas Commun.

152

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Today's Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3/4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks-from regional CDN servers to peer CDN servers (e.g., smartrouters in users' homes)-to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world datasets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over 2 weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors, which determine the request patterns. Using trace-driven experiments, we compare strategies for edge content caching including LRU and LFU, in terms of supporting mobile video requests. We reveal that content, location and mobility factors all affect edge content caching performance. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance. The results show that our design significantly improves the cache hit rate by up to 30% compared with LRU/LFU. Index Terms-Edge network, mobile video streaming, user behavior, measurement, content delivery.

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Understanding User Experience of Mobile Video: Framework, Measurement, and Optimization

Cited by 16 publications

References 99 publications

Survey and Challenges of QoE Management Issues in Wireless Networks

Survey and Challenges of QoE Management Issues in Wireless Networks

Delivery quality score model for Internet video

Understanding Performance of Edge Content Caching for Mobile Video Streaming

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