QoE-Aware Video Delivery in Multimedia IoT Network with Multiple Eavesdroppers

Abstract

How to deal with the increasing video traffic and diverse service demands while ensuring the security of transmission is an open issue in the multimedia Internet of Things (IoT). This paper addresses this issue and studies a secure delivery scheme under a multicast scenario in the presence of multiple eavesdroppers where small base stations (SBSs) can send videos to users cooperatively. Aiming at potential eavesdroppers, a channel model including artificial noise is introduced to reduce the harm of illegal data acquisition. A network quality of experience (QoE) optimization problem is first formulated to account for video quality and delivery delay. In order to solve the nonconvex problem, the successive convex approximation (SCA) technique is applied to optimize multicast group beamforming, reduce the possibility of multicast video eavesdropping, and select video quality where a heuristic scheme is proposed to maximize the network QoE. The effectiveness of the proposed scheme is finally validated by extensive simulations in terms of algorithm convergence performance and network QoE-enhanced performance.

Bayesian K-means clustering based quality of experience aware multimedia video streaming

“…However this approach did not control the long-term bandwidth fluctuation problems. Wu et al [17] developed a plan for QoE-aware video delivery in multimedia IoT networks by potential eavesdroppers. In this method, the selection of joint video quality as well as multicast group beamforming plan was developed to enhance a QoE network at the time of removing the eavesdropping as much as possible.…”