A novel paradigm for context-aware content pre-fetching in mobile networks

Kamaraju, Pavan; Lungaro, Pietro; Segall, Zary

doi:10.1109/wcnc.2013.6555309

Cited by 7 publications

(4 citation statements)

References 10 publications

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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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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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“…The second strategy we consider is one that can be implemented directly by a content provider, without ISP assistance. The basic idea is to offload the mobile traffic to a fixedline broadband operator by using speculative pre-fetching strategies such as the ones extensively explored in previous literature [18], [24], [15], [9], [7], [28], [11]. Alternately, a pair of fixed-line and mobile providers can collaborate with each other to aggresively pre-fetch content expected to be accessed by the user over a mobile network, and push it to a devicelocal cache controlled by the mobile network operator (e.g., pushed to reserved storage on the phone).…”

Section: B Speculative Prefetchingmentioning

confidence: 99%

On factors affecting the usage and adoption of a nation-wide TV streaming service

Karamshuk

Sastry

Secker³

et al. 2015

2015 IEEE Conference on Computer Communications (INFOCOM)

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Using nine months of access logs comprising 1.9 Billion sessions to BBC iPlayer, we survey the UK ISP ecosystem to understand the factors affecting adoption and usage of a high bandwidth TV streaming application across different providers. We find evidence that connection speeds are important and that external events can have a huge impact for live TV usage. Then, through a temporal analysis of the access logs, we demonstrate that data usage caps imposed by mobile ISPs significantly affect usage patterns, and look for solutions. We show that product bundle discounts with a related fixed-line ISP, a strategy already employed by some mobile providers, can better support user needs and capture a bigger share of accesses. We observe that users regularly split their sessions between mobile and fixed-line connections, suggesting a straightforward strategy for offloading by speculatively pre-fetching content from a fixed-line ISP before access on mobile devices.

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“…Specially, Higgins et al [20] presented a cost-benefit analysis to decide when to prefetch based on the performance such as latency reduction, the cost of energy and monetary cost or data usage. Some prefetching schemes [9], [21], [22] also take advantage of the users' spatiotemporal access patterns for web contents. Lymberopoulos et al [9] presented a prefetching scheme that predicts what webpages a user is likely to request as well as when these requests are likely to occur.…”

Section: B Mobile Prefetchmentioning

confidence: 99%

“…Parate et al [21] proposed an approach to predict which app will be used next and when it will be used, and then prefetch application content to fast app launch. Kamaraju et al [22] presented a context-aware delivery paradigm that prefetches video contents by exploiting locations and times in which the networks experience excess of resources. However none of the prior prefetching schemes are designed for news webpage prefetching and make prefetch decisions based on the network condition prediction.…”

Section: B Mobile Prefetchmentioning

confidence: 99%

Network agile preference-based prefetching for mobile devices

Han

Jung

et al. 2014

2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC)

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For mobile devices, communication via cellular networks consumes more energy than via WiFi networks, and suffers an expensive limited data plan. On the other hand, as the coverage and the density of WiFI networks are smaller than those of the cellular networks, users cannot purely rely on WiFi to access the Internet. In this work we present a behavior-aware and preference-based approach to prefetch news webpages for the user to visit in the near future, by exploiting the WiFi network connections to reduce the energy and monetary cost. We first design an efficient preference learning algorithm to keep track of the user's changing interests, and then by predicting the appearance and durations of the WiFi network connections, our prefetch approach optimizes when to prefetch to maximize the user experience while lowing the prefetch cost. Our prefetch approach also exploits the idle period of WiFi connections to reduce the tail-energy consumption. We implement our approach in iPhone and our extensive evaluations show that our system achieves about 60% hit ratio, saves about 50% cellular data usage, and reduces the energy cost by 7%.

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A novel paradigm for context-aware content pre-fetching in mobile networks

Cited by 7 publications

References 10 publications

Eye-gaze Based Service Provision and QoE Optimization

Eye-gaze Based Service Provision and QoE Optimization

On factors affecting the usage and adoption of a nation-wide TV streaming service

Network agile preference-based prefetching for mobile devices

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