TY - CHAP
T1 - Learning Popularity for Proactive Caching in Cellular Networks
AU - Doan, Khai Nguyen
AU - Van Nguyen, Thang V.
AU - Quek, Tony Q.S.
N1 - Publisher Copyright:
© Cambridge University Press 2020.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Video data have been showed to dominate a significant portion of mobile data traffic and have a strong influence on a backhaul congestion issue in cellular networks. To tackle the problem, proactive caching is considered as a prominent candidate in terms of cost efficiency. In this chapter, we study a novel popularity-predicting-based caching procedure that takes raw video data as input to determine an optimal cache placement policy, which deals with both published and unpublished videos. For dealing with unpublished videos whose statistical information is unknown, features from the video content are extracted and condensed into a high-dimensional vector. This type of vector is then mapped to a lower-dimensional space. This process not only alleviates the computational burden but also creates a new vector that is more meaningful and comprehensive. At this stage, different types of prediction models can be trained to anticipate the popularity, for which information from published videos is used as training data.
AB - Video data have been showed to dominate a significant portion of mobile data traffic and have a strong influence on a backhaul congestion issue in cellular networks. To tackle the problem, proactive caching is considered as a prominent candidate in terms of cost efficiency. In this chapter, we study a novel popularity-predicting-based caching procedure that takes raw video data as input to determine an optimal cache placement policy, which deals with both published and unpublished videos. For dealing with unpublished videos whose statistical information is unknown, features from the video content are extracted and condensed into a high-dimensional vector. This type of vector is then mapped to a lower-dimensional space. This process not only alleviates the computational burden but also creates a new vector that is more meaningful and comprehensive. At this stage, different types of prediction models can be trained to anticipate the popularity, for which information from published videos is used as training data.
UR - http://www.scopus.com/inward/record.url?scp=85192928311&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85192928311&partnerID=8YFLogxK
U2 - 10.1017/9781108691277.008
DO - 10.1017/9781108691277.008
M3 - Chapter
AN - SCOPUS:85192928311
SN - 9781108480833
SP - 127
EP - 145
BT - Wireless Edge Caching
PB - Cambridge University Press
ER -