Paper Info
Title
Asymptotic Optimal Edge Resource Allocation for Video Streaming via User Preference Prediction
Abstract
Mobile edge computing extends computing and storage resources to the proximity of mobile users, facilitating a number of innovative mobile applications. Particularly, video streaming is the most prevailing one that consumes substantial edge resources. In this paper, we investigate the multi-dimensional resource allocation for video service provisioning, with the objective of ensuring satisfied streaming experience at high resource utilization. Considering the diversified and constantly changing user preferences on the quality of video contents, the edge resource allocation process is modeled as a long-term utility maximization problem. To address this problem, we propose an online learning algorithm that actively estimates user preferences according to regression analysis on user feedback. This algorithm requires no training phase, and hence is adaptive to dynamic user interests and available edge resources. Both theoretical analysis and numerical results demonstrate that the performance of the proposed algorithm asymptotically approaches the hindsight optimal resource allocation strategy.
Year
DOI
Venue
2019
10.1109/ICC.2019.8762002
IEEE International Conference on Communications
Field
DocType
ISSN
Online learning,Service provisioning,Computer science,Regression analysis,Video streaming,Computer network,Utility maximization problem,Resource allocation,Mobile edge computing,Hindsight bias
Conference
1550-3607
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Peng Yang18520.75
Ning Zhang229917.82
Shan Zhang336525.66
Feng Lv431328.56
Li Yu528348.76
Xuemin Shen615389928.67