A resource allocation framework for adaptive video streaming over LTE. (1st November 2017)

• Record Type: Journal Article
• Authors: Kumar, Satish; Sarkar, Arnab; Sur, Arijit
• Is Part Of: Journal of network and computer applications. Volume 97(2017)
• Page Start: 126
• Page End: 139
• Publication Date: 2017-11-01
• Subjects: Mobile communication -- Adaptive video streaming -- Radio resource management -- Quality of Experience (QoE) -- Long Term Evolution (LTE); Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2017.08.015
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: The ever-increasing demand for multimedia applications on mobile devices is set to pose considerable challenges on the bandwidth allocation mechanisms in LTE and future networks. In this work, we present " Adaptive Video Streaming Architecture ", an efficient resource allocation framework that attempts to maximize Quality of Experience (QoE) for a set of users by simultaneously balancing three important QoE verticals: (i) providing stutter-free viewing experience by dynamically throttling video streaming data-rates; (ii) minimizing flickers in video outputs by controlling and stabilizing the rates of encoding quality switches over time; (iii) maximizing aggregate video quality over all users. We have posed the resource allocation problem through an Integer Linear Programming (ILP) formulation and showed that solution strategies such as conventional dynamic programming ( DP ) impose substantial overheads. Next, we have proposed the Streamlined DP-based Quality-level Allocator (SDQA) which utilizes the discrete nature in the data-rate scalability of video flows to retain a far lower number of non-dominating partial DP -solutions and thereby generates far quicker solutions. Further, SDQA has been extended to provide a tunable approximation scheme called SDQA-AA that can accelerate the speed of generating solutions at multiple scales with distinct bounds on solution quality. Analytical and simulation based experimental results are promising.