Over-The-Top Catch-up TV content-aware caching

Nogueira, João; González, Daniel González; Guardalben, Lucas; Sargento, Susana

doi:10.1109/iscc.2016.7543869

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…On the other hand, there have been many studies on cache update [ 14 , 15 , 16 , 17 , 18 , 19 ]. The performance of FIFO, the least recently used (LRU), and the least frequently used (LFU) schemes was analyzed in terms of the rate at which a particular request is returned before a given deadline [ 14 ] and in terms of hit rate [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

“…The performance of FIFO, the least recently used (LRU), and the least frequently used (LFU) schemes was analyzed in terms of the rate at which a particular request is returned before a given deadline [ 14 ] and in terms of hit rate [ 15 ]. A novel content-aware cache replacement algorithm taking advantage of content demand forecasts was investigated to efficiently use limited caches in size [ 16 ]. LRU-K, which is a combination of LRU and LFU, was proposed [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

“…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%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Kim¹,

Lee

Ryu

et al. 2021

Sensors

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“…• Least frequently used (LFU) depends on the frequency of requests as an indicator of the popularity of content where the content with the lowest request count is evicted from the cache to make room for new content. LFU suffers from gradual performance degradation because previously-cached unpopular content which received a high number of historic requests remains in the cache, such a phenomenon is known as cache pollution [18].…”

Section: A Traditional Caching Schemementioning

confidence: 99%

“…As mentioned earlier, the number of viewers is usually used to denote videos popularity, but some researchers suggest other popularity measures such in [18] and [61]. Nogueira et al [18] introduced expected priority and designed a caching scheme that favored TV shows with a greater expected priority, at the expense of others with lower expected priorities. Higher priorities map to higher expectations that an item is going to be requested in the future.…”

Section: ) Models Based On Metadatamentioning

confidence: 99%

Popularity-Based Video Caching Techniques for Cache-Enabled Networks: A Survey

et al. 2019

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The proliferation of the mobile Internet and connected devices, which offer a variety of services at different levels of performance is a major challenge for the fifth generation of wireless networks and beyond. Innovative solutions are needed to leverage recent advances in machine storage/memory, context awareness, and edge computing. Cache-enabled networks and techniques such as edge caching are envisioned to reduce content delivery times and traffic congestion in wireless networks. Only a few contents are popular, accounting for the majority of viewers, so caching them reduces the latency and download time. However, given the dynamic nature of user behavior, the integration of popularity prediction into caching is of paramount importance to better network utilization and user satisfaction. In this paper, we first present an overview of caching in wireless networks and then provide a detailed comparison of traditional and popularity-based caching. We discuss the attributes of videos and the evaluation criteria of caching policies. We summarize some of the recent work on proactive caching, focusing on prediction strategies. Finally, we provide insight into the potential opportunities and challenges as well as some open research problems enable the realization of efficient deployment of popularity-based caching as part of the next-generation mobile networks. INDEX TERMS 5G, cache-enabled networking, popularity prediction, proactive caching, videos popularity.

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