QoE Aware Resource Allocation for Video Communications over LTE Based Mobile Networks
Journal Title: EAI Endorsed Transactions on Cloud Systems - Year 2016, Vol 2, Issue 7
Abstract
As the limits of video compression and usable wireless radio resources are exhausted, providing increased protection to critical data is regarded as a way forward to increase the effective capacity for delivering video data. This paper explores the provisioning of selective protection in the physical layer to critical video data and evaluates its effectiveness when transmitted through a wireless multipath fading channel. In this paper, the transmission of HEVC encoded video through an LTE-A wireless channel is considered. HEVC encoded video data is ranked based on how often each area of the picture is referenced by subsequent frames within a GOP in the sequence. The critical video data is allotted to the most robust OFDM resource blocks, which are the radio resources in the time-frequency domain of the LTE-A physical layer, to provide superior protection. The OFDM resource blocks are ranked based on a prediction for their robustness against noise. Simulation results show that the proposed content aware resource allocation scheme helps to improve the objective video quality up to 37dB at lower channel SNR levels when compared against the reference system, which treats video data uniformly. Alternatively, with the proposed technique the transmitted signal power can be lowered by 30% without sacrificing video quality at the receiver.
Authors and Affiliations
Ryan Perera, Anil Fernando, Thanuja Mallikarachchi, Hemantha Kodikara Arachchi, Mahsa Pourazad
Keywords
Related Articles
- EP ID EP45579
- DOI http://dx.doi.org/10.4108/icst.qshine.2014.256431
- Views 259
- Downloads 0
A New QoE-Driven Video Cache Allocation Scheme for Mobile Cloud Server
With the advent of mobile cloud computing, video cache technologies at local cellular networks have attracted extensive attention. Nevertheless, existing video cache allocation schemes mostly made decisions only accordin...
Overview - Fog Computing and Internet-of-Things (IOT)
The Internet today is getting connected to a very large number of devices or sensors of IOT. It is expected that 50 billion devices will be connected to the Internet by 2020..The IOT driven global economy will have many...
Database Migration on Premises to AWS RDS
For the past four decades, the traditional relational databases have been in use in Information Technology industry. There was a phenomenal conversion in the IT industry in terms of commercial applications in the previou...
PETFEN: A Performance Evaluation Tool for Flow-Level Network Modeling of Ethernet Networks
We present in this paper PETFEN, a Performance Evaluation Tool for Flow-level network modeling of Ethernet Networks. Flow-level network models are a useful tool to dimension and predict various performances of networks w...
BETaaS platform – a Things as a Service Environment for future M2M marketplaces
Building the Environment for Things as a Service (BETaaS) is a novel platform for the deployment and execution of contentcentric Machine-to-Machine (M2M) applications, which relies on a local cloud of gateways. BETaaS pl...