TY - GEN
T1 - APRANK
T2 - 2017 IEEE International Conference on Multimedia and Expo (ICME 2017)
AU - Ma, Ge
AU - Wang, Zhi
AU - Chen, Minghua
AU - Zhu, Wenwu
PY - 2017/8/28
Y1 - 2017/8/28
N2 - Today's internet has witnessed a fast growth of mobile video streaming. Different from traditional PC/laptop-based video streaming, mobile video streaming relies on the usage of mobile devices and wireless networks, allowing people to receive video content on the move. The change has challenged traditional video content delivery, which uses centralized infrastructure (e.g., CDN) inside the network for content distribution, in a sense that mobile users (connected to Wi-Fi or cellular networks) encounter large delay and small download speed. One promising solution is to prefetch content in the edge of the network, e.g., on access points (APs). However, it faces the great challenges: 1) It is difficult to prefetch content in such edge APs with limited storage capacity; 2) Users' mobility cross APs affects the content delivery; 3) Popularity of content may change significantly across APs. Previous approaches make mobile video content delivery inefficient without jointly considering these problems. In this paper, we propose an AP-assisted mobile video delivery framework to solve these problems. First, using large-scale measurement studies of users' trajectories and preferences of videos, we reveal that both users' mobility patterns and their intrinsic preferences are important for AP-assisted content delivery. Second, we formulate the AP content prefetching as an optimization problem, and develop an online solution, APRank, to solve it. Third, we evaluate the effectiveness of our design, compared with four baselines, random-based, popularity-based, preference-based and offline prefetching.
AB - Today's internet has witnessed a fast growth of mobile video streaming. Different from traditional PC/laptop-based video streaming, mobile video streaming relies on the usage of mobile devices and wireless networks, allowing people to receive video content on the move. The change has challenged traditional video content delivery, which uses centralized infrastructure (e.g., CDN) inside the network for content distribution, in a sense that mobile users (connected to Wi-Fi or cellular networks) encounter large delay and small download speed. One promising solution is to prefetch content in the edge of the network, e.g., on access points (APs). However, it faces the great challenges: 1) It is difficult to prefetch content in such edge APs with limited storage capacity; 2) Users' mobility cross APs affects the content delivery; 3) Popularity of content may change significantly across APs. Previous approaches make mobile video content delivery inefficient without jointly considering these problems. In this paper, we propose an AP-assisted mobile video delivery framework to solve these problems. First, using large-scale measurement studies of users' trajectories and preferences of videos, we reveal that both users' mobility patterns and their intrinsic preferences are important for AP-assisted content delivery. Second, we formulate the AP content prefetching as an optimization problem, and develop an online solution, APRank, to solve it. Third, we evaluate the effectiveness of our design, compared with four baselines, random-based, popularity-based, preference-based and offline prefetching.
UR - http://www.scopus.com/inward/record.url?scp=85030232736&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85030232736&origin=recordpage
U2 - 10.1109/ICME.2017.8019404
DO - 10.1109/ICME.2017.8019404
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781509060689
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
SP - 7
EP - 12
BT - 2017 IEEE International Conference on Multimedia and Expo (ICME)
PB - IEEE
Y2 - 10 July 2017 through 14 July 2017
ER -