Adaptive-Buffer Power Save Mechanism for Mobile Multimedia Streaming

Adams, Janet; Muntean, Gabriel‐Miro

doi:10.1109/icc.2007.751

Cited by 39 publications

(15 citation statements)

References 35 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other solutions allowing the WNIC to sleep for a longer periods of time, include buffering the incoming data on the mobile device [152], or buffering the data corresponding to several beacons and releasing them at once after the mobile device wakes up several times to check if there are any packets for it [153]. The latter is achieved by introducing an additional buffer to the one already included in the Access Point.…”

Section: ) Content Delivery-related Solutionsmentioning

confidence: 99%

Energy-Aware Mobile Learning:Opportunities and Challenges

Moldovan

Weibelzahl

Muntean

2014

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

Abstract-As mobile devices are becoming more powerful and affordable they are increasingly used for mobile learning activities. By enabling learners' access to educational content anywhere and anytime, mobile learning has both the potential to provide online learners with new opportunities, and to reach less privileged categories of learners that lack access to traditional e-learning services. Among the many challenges with mobile learning, the battery-powered nature of mobile devices and in particular their limited battery life, stands out as one issue that can significantly limit learners' access to educational content while on the move. Adaptation and personalisation solutions have widely been considered for overcoming the differences between learners and between the characteristics of their mobile devices. However, while various energy saving solutions have been proposed in order to provide mobile users with extended device usage time, the areas of adaptive mobile learning and energy conservation in wireless communications failed to meet under the same umbrella. This paper bridges the two areas by presenting an overview of adaptive mobile learning systems as well as how these can be extended to make them energy-aware. Furthermore, the paper surveys various approaches for energy measurement, modelling and adaptation, three major aspects that have to be considered in order to deploy energy-aware mobile learning systems. Discussions on the applicability and limitations of these approaches for mobile learning are also provided.

show abstract

Section: ) Content Delivery-related Solutionsmentioning

confidence: 99%

Energy-Aware Mobile Learning:Opportunities and Challenges

Moldovan

Weibelzahl

Muntean

2014

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…This can reduce the total network transmission for downloading the video file. Note the network interface card could consume 30% to 40% of the total battery power consumed during a streaming session to a mobile device [15], [16].…”

Section: Video Playback Duration File Size and Formatsmentioning

confidence: 99%

Measurement and Analysis of an Internet Streaming Service to Mobile Devices

Liu

Guo

et al. 2013

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

Abstract-ReceivingInternet streaming services on various mobile devices is getting increasingly popular, and cloud platforms have also been gradually employed for delivering streaming services to mobile devices. While a number of studies have been conducted at the client side to understand and characterize Internet mobile streaming delivery, little is known about the server side, particularly for the recent cloud-based Internet mobile streaming delivery. In this work, we aim to investigate the Internet mobile streaming service at the server side. For this purpose, we have collected a four-month server-side log on the cloud (with 1,002 TB delivered video traffic) from a top Internet mobile streaming service provider serving worldwide mobile users. Through trace analysis, we find that (1) a major challenge for providing Internet mobile streaming services is rooted from the mobile device hardware and software heterogeneity. In this workload, we find over 3,400 different hardware models with more than 100 different screen resolutions running 14 different mobile OS and 3 audio codecs and 4 video codecs. (2) To deal with the device heterogeneity, CPU-intensive transcoding is used on the cloud to customize the video to the appropriate versions at runtime for different devices. A video clip could be transcoded into more than 40 different versions in order to serve requests from different devices. (3) Compared to videos in traditional Internet streaming, mobile streaming videos are typically of much smaller size (a median of 1.68 MBytes) and shorter duration (a median of 2.7 minutes). Furthermore, the daily mobile user accesses are more skewed following a Zipf-like distribution but users' interests also quickly shift. Considering the huge demand of CPU cycles for online transcoding, we further examine server-side caching in order to reduce the total CPU cycle demand from the cloud. We show that a policy considering different versions of a video altogether outperforms other intuitive ones when the cache size is limited.

show abstract

“…Solutions include buffering the incoming data on the mobile device, or buffering the data corresponding so several beacon intervals at an intermediate node and releasing it only after the mobile device wakes up several times to check for incoming data [22]. To maintain the WLAN interface in sleep mode for significant periods of time during VoIP calls, the GreenCall algorithm uses sleep and wake-up schedules, where the sleep periods are computed based on the maximum delay that a user can tolerate during a conversation [23].…”

Section: A Multimedia Content Adaptationmentioning

confidence: 99%

Subjective Assessment of BitDetect—A Mechanism for Energy-Aware Multimedia Content Adaptation

Moldovan

Muntean

2012

IEEE Trans. on Broadcast.

View full text Add to dashboard Cite

Abstract-As mobile devices are becoming more compact and powerful and as they start to be increasingly used for accessing power-hungry multimedia streaming applications, there is an increasing need for mechanisms to efficiently manage the limited battery power resources. This is especially important as the battery capacity has not kept up with the power requirements of an increasing number of mobile device features and "always on" connected users. Adaptive multimedia-based power-saving mechanisms often decrease the clip bitrate to increase the mobile device battery life, without considering the effect of these degradations on the user-perceived quality. This paper proposes BitDetect, a mechanism that uses objective video quality assessment metrics to detect content-specific video bitrate levels that enable saving battery power while maintaining good user perceived quality. Results from a subjective study indicate that the recommended bitrate offers good user-perceived quality across different multimedia clips. Furthermore, experimental tests indicate that significant battery power can be saved by using the recommended bitrates when streaming multimedia clips to a mobile device.Index Terms-Battery power saving, bitrate estimation, objective assessment metrics, subjective video quality assessment.

show abstract

Adaptive-Buffer Power Save Mechanism for Mobile Multimedia Streaming

Cited by 39 publications

References 35 publications

Energy-Aware Mobile Learning:Opportunities and Challenges

Energy-Aware Mobile Learning:Opportunities and Challenges

Measurement and Analysis of an Internet Streaming Service to Mobile Devices

Subjective Assessment of BitDetect—A Mechanism for Energy-Aware Multimedia Content Adaptation

Contact Info

Product

Resources

About