Energy Consumption Reduction via Context-Aware Mobile Video Pre-fetching

Devlić, Alisa; Lungaro, Pietro; Kamaraju, Pavan; Segall, Zary; Tollmar, Konrad

doi:10.1109/ism.2012.56

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…Another potential solution to improving adaptation efficiency is to pre-fetch the most popular content ahead of time for a specific user if his/her interest in this content may be known a priori or inferred from his previous browsing history. 15 As a result, multimedia adaptation can be performed in advance.…”

Section: Resultsmentioning

confidence: 99%

Context-Aware Multimedia Content Adaptation for Mobile Web

Zhang¹,

Jangam²,

Zhou³

et al. 2015

IJNDC

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Multimedia content in web pages poses a variety of problems to mobile web users, such as incompatibility with mobile devices or failing to meet accessibility needs of users with vision disabilities. To address those problems, this study proposes and evaluates a context-aware approach to multimedia content adaptation for mobile Web. It uniquely takes into consideration not only users' preferences and accessibility needs, but also capabilities of multimedia support of specific mobile devices. Evaluation shows that the proposed approach is accurate and efficient. This study provides insights for research on context-aware mobile computing, adaptive interfaces, and mobile HCI.

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Section: Resultsmentioning

confidence: 99%

Context-Aware Multimedia Content Adaptation for Mobile Web

Zhang¹,

Jangam²,

Zhou³

et al. 2015

IJNDC

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“…Since end-to-end RTT could pose significant limitations on the performances achievable by foveated content provision in real network settings, an important step for our next generation testbed is a complete system deployment with physically separated server and user sides. Further, the possibility of contextually adapting the service provision parameters to the underlying network conditions can bring additional improvements to the achievable degree of resource optimization and it is part of our ongoing efforts [18][19][20][21].…”

Section: Discussionmentioning

confidence: 99%

Eye-gaze Based Service Provision and QoE Optimization

Lungaro¹,

Tollmar²

2016

5th ISCA/DEGA Workshop on Perceptual Quality of Systems (PQS 2016)

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This paper presents an innovative approach to the optimization of multimedia content delivery for future data intensive services, featuring an eye-gaze based paradigm for mobile video provision. The proposed solution uses real-time eyegaze information, collected by connected eye-trackers, to provide high resolution in the portion of a video frame corresponding to the central part of a user's field of view and lower quality in the areas corresponding to the user's peripheral vision. This approach brings new optimization opportunities, leading to significant reduction in terms of network and device resources needed to deliver high quality experience to the users. The initial results showed potential bandwidth savings on the order of 60%, while maintaining high levels of user perceived video quality. Relevant areas of application include head mounted displays for mobile virtual and augmented reality, where substantial resource consumption savings are anticipated due to their high screen resolutions. The paper also introduces and discusses few architectural embodiments as well as additional services enabled by this technology. The main focus is on utilizing the vast amount of eye-gaze information, accumulated during content provision, to build representative models for individual users' attention and content consumption behaviors.

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“…Hence the energy consumption is very high in order to stream online video content (Devlic et al, 2012;Hoque et al, 2013). This is due to many variations on online video attributes.…”

Section: Problem Statementsmentioning

confidence: 99%

Quality of Experience (QOE) Aware Video Attributes Determination for Mobile Streaming Using Hybrid Profiling

Jofri

Fudzee

Ismail

et al. 2017

IJEECS

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<p>Today, consumers use a smartphone device to display the media contents for work and entertainment purposes, as well as watching online video. Online video streaming is the main cause that consume smartphone’s energy quickly. To overcome this problem, smartphone’s energy management is crucial. Thus, a hybrid energy-aware profiler is proposed. Basically, a profiler will monitor and manage the energy consumption in the smartphone devices. The hybrid energy-aware profiler will set up a protocol preference of both the user and the device. Then, it will estimates the energy consumption in smartphone. However, saving energy alone can contribute to the Quality of Experience (QoE) neglection, thus the proposed solution takes into account the client QoE. Even though there are several existing energy-aware profilers that have been developed to manage energy use in smartphones however, most energy-aware profilers does not consider QoE at the same time. The proposed solution consider both, the performance of the hybrid energy-aware profiler is compared with the baseline energy models against a variation of content adaptation according to the pre-defined variables. Three types of variables were determined; resolution, frame rate and energy consumption in smartphone devices. In this area, QoE subjective methods based on MOS (Mean Opinion Score) are the most commonly used approaches for defining and quantifying real video quality. Nevertheless, although these approaches have been established to consistently quantify users’ amounts of approval, they do not adequately realize which are the criteria of video attribute that important. In this paper, we conducted an experiment with a certain devices to measures user’s QoE and energy usage of video attribute in smartphone devices. Our results demonstrate that the list of possible solution is a relevant and useful video attribute that satify the users.</p>

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Energy Consumption Reduction via Context-Aware Mobile Video Pre-fetching

Cited by 9 publications

References 6 publications

Context-Aware Multimedia Content Adaptation for Mobile Web

Context-Aware Multimedia Content Adaptation for Mobile Web

Eye-gaze Based Service Provision and QoE Optimization

Quality of Experience (QOE) Aware Video Attributes Determination for Mobile Streaming Using Hybrid Profiling

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