Caching for IPTV distribution with time-shift

Abrahamsson, Henrik; Björkman, Mats

doi:10.1109/iccnc.2013.6504212

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…One way to reduce the network load is to cache popular content closer to the users [44][45][46][47][48]. Caching is a well-studied technique for web content and video but TV is different in many ways.…”

Section: Caching For Tv On Demandmentioning

confidence: 99%

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Resource management in radio access and IP-based core networks for IMT Advanced and Beyond

Song

Hidell

Abrahamsson³

et al. 2013

Sci. China Inf. Sci.

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Optimal resource allocation for transmission diversity in multi-radio access networks: a coevolutionary genetic algorithm approach SCIENCE CHINA Information Sciences 57, 022310 (2014); Efficient resource allocation scheme for multi-service based on interference mitigation in LTE-Advanced networks SCIENCE CHINA Information Sciences 57, 082304 (2014); Optimal remote radio head selection for cloud radio access networks SCIENCE CHINA Information Sciences 59, 102315 (2016); Novel spectrum sensing and access in cognitive radio networks SCIENCE CHINA Information Sciences 61, 089302 (2018);. RESEARCH PAPER .

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Section: Caching For Tv On Demandmentioning

confidence: 99%

“…The simulations are based on real TV schedules, and statistics about TV program popularity and viewer activity. The simulator and the simulation results are described in detail in [47,48].…”

Section: Simulation Of Iptv With Time-shiftmentioning

confidence: 99%

Resource management in radio access and IP-based core networks for IMT Advanced and Beyond

Song

Hidell

Abrahamsson³

et al. 2013

Sci. China Inf. Sci.

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“…In the new video streaming system, multiple different video contents can be transmitted in one transmission if prerequisites are satisfied, and transmission efficiency can be thus significantly improved. Cache update is an important factor in video streaming systems [ 14 , 15 , 16 , 17 , 18 , 19 ]. However, there have been no previous studies that investigated cache update policies for the index coding-based video streaming system.…”

Section: Introductionmentioning

confidence: 99%

A New Cache Update Scheme Using Reinforcement Learning for Coded Video Streaming Systems

Kim¹,

Lee

Ryu

et al. 2021

Sensors

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As the demand for video streaming has been rapidly increasing recently, new technologies for improving the efficiency of video streaming have attracted much attention. In this paper, we thus investigate how to improve the efficiency of video streaming by using clients’ cache storage considering exclusive OR (XOR) coding-based video streaming where multiple different video contents can be simultaneously transmitted in one transmission as long as prerequisite conditions are satisfied, and the efficiency of video streaming can be thus significantly enhanced. We also propose a new cache update scheme using reinforcement learning. The proposed scheme uses a K-actor-critic (K-AC) network that can mitigate the disadvantage of actor-critic networks by yielding K candidate outputs and by selecting the final output with the highest value out of the K candidates. The K-AC exists in each client, and each client can train it by using only locally available information without any feedback or signaling so that the proposed cache update scheme is a completely decentralized scheme. The performance of the proposed cache update scheme was analyzed in terms of the average number of transmissions for XOR coding-based video streaming and was compared to that of conventional cache update schemes. Our numerical results show that the proposed cache update scheme can reduce the number of transmissions up to 24% when the number of videos is 100, the number of clients is 50, and the cache size is 5.

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“…In contrast, we model user behavior and access patterns for a large-scale IPTV system using large volume of real user data. Recent studies on user behaviors of CDN IPTV networks have focused independently on either live TV [Sripanidkulchai et al 2004;Veloso et al 2006;Agrawal et al 2007;Cha et al 2008;Qiu et al 2009bQiu et al , 2009a or VoD [Cherkasova and Gupta 2004;Yu et al 2006;Cheng et al 2008;Choi et al 2012;Abrahamsson and Bjorkman 2013]. One of the first measurement studies is Yu et al [2006], where the authors studied the traces of a large CDN VoD system and introduced a modified Poisson distribution that more accurately modeled the user arrival process.…”

Section: Related Workmentioning

confidence: 99%

“…The work in Smith [2007], Annapureddy et al [2007], Mu et al [2012], and Abrahamsson and Bjorkman [2013] presents some possible models for user interactive behavior. However, they are not based on real data.…”

Section: Related Workmentioning

confidence: 99%

Dissecting User Behaviors for a Simultaneous Live and VoD IPTV System

Liu

Cui

Chan

et al. 2014

ACM Trans. Multimedia Comput. Commun. Appl.

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IPTV services deployed nowadays often consist of both live TV and Video-on-Demand (VoD), offered by the same service provider to the same pool of users over the same managed network. Understanding user behaviors in such a setting is hence an important step for system modelling and optimization. Previous studies on user behavior on video services were on either live TV or VoD.For the first time, we conduct an in-depth large-scale behavior study for IPTV users offering simultaneously live TV and VoD choices at the same time. Our data is from the largest IPTV service provider in China, offering hundreds of live channels and hundreds of thousands of VoD files, with traces covering more than 1.9 million users over a period of 5 months. This large dataset provides us a unique opportunity to cross-compare user viewing behaviors for these services on the same platform, and sheds valuable insights on how users interact with such a simultaneous system.Our results lead to new understanding on IPTV user behaviors which have strong implications on system design. For example, we find that the average holding time for VoD is significantly longer than live TV. live TV users tend to surf more. However, if such channel surfing is discounted, the holding times of both services are not much different. While users in VoD tend to view HD longer, channel popularity for live TV is much less dependent on its video quality. In contrast to some popular assumptions on user interactivity, the transitions among live TV, VoD, and offline modes are far from a Markov model.

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Caching for IPTV distribution with time-shift

Cited by 11 publications

References 24 publications

Resource management in radio access and IP-based core networks for IMT Advanced and Beyond

Resource management in radio access and IP-based core networks for IMT Advanced and Beyond

A New Cache Update Scheme Using Reinforcement Learning for Coded Video Streaming Systems

Dissecting User Behaviors for a Simultaneous Live and VoD IPTV System

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