Fuzzy logic inference system-based hybrid quality prediction model for wireless 4kUHD H.265-coded video streaming

Networked visual applications such video streaming have grown exponentially in recent years, yet are known to be sensitive to network impairments. However, available measurement techniques that adopt a full reference model are impractical in real-time streaming because they require the original video sequence available at the receivers side. The primary aim of this study is to present a hybrid no-reference prediction model for the perceptual quality of 4kUHD H.265-coded video in the wireless domain. The contributions of this paper are two-fold: first, an investigation of the impact of quality of service (QoS) parameters on 4kUHD H.265-coded video transmission in an experimental environment; second, objective model based on fuzzy logic inference system is developed to predict the visual quality by mapping QoS parameters to the measured quality of experience. The model is evaluated in contrast to random neural networks. The results show that good prediction accuracy was obtained from the proposed hybrid prediction model. This study will help in the development of a reference-free video quality prediction model and QoS control methods for 4kUHD video streaming

Fuzzy logic inference system-based hybrid quality prediction model for wireless 4kUHD H.265-coded video streaming

Networked visual applications such video streaming have grown exponentially in recent years, yet are known to be sensitive to network impairments. However, available measurement techniques that adopt a full reference model are impractical in real-time streaming because they require the original video sequence available at the receivers side. The primary aim of this study is to present a hybrid no-reference prediction model for the perceptual quality of 4kUHD H.265-coded video in the wireless domain. The contributions of this paper are two-fold: first, an investigation of the impact of quality of service (QoS) parameters on 4kUHD H.265-coded video transmission in an experimental environment; second, objective model based on fuzzy logic inference system is developed to predict the visual quality by mapping QoS parameters to the measured quality of experience. The model is evaluated in contrast to random neural networks. The results show that good prediction accuracy was obtained from the proposed hybrid prediction model. This study will help in the development of a reference-free video quality prediction model and QoS control methods for 4kUHD video streaming

Live media production: multicast optimization and visibility for clos fabric in media data centers

Media production data centers are undergoing a major architectural shift to introduce digitization concepts to media creation and media processing workflows. Content companies such as NBC Universal, CBS/Viacom and Disney are modernizing their workflows to take advantage of the flexibility of IP and virtualization. In these new environments, multicast is utilized to provide point-to-multi-point communications. In order to build point-to-multi-point trees, Multicast has an established set of control protocols such as IGMP and PIM. The existing multicast protocols do not optimize multicast tree formation for maximizing network throughput which lead to decreased fabric utilization and decreased total number of admitted flows. In addition, existing multicast protocols are not bandwidth-aware and could cause links to over-subscribe leading to packet loss and lower video quality. TV production traffic patterns are unique due to ultra high bandwidth requirements and high sensitivity to packet loss that leads to video impairments. In such environments, operators need monitoring tools that are able to proactively monitor video flows and provide actionable alerts. Existing network monitoring tools are inadequate because they are reactive by design and perform generic monitoring of flows with no insights into video domain. The first part of this dissertation includes a design and implementation of a novel Intelligent Rendezvous Point algorithm iRP for bandwidth-aware multicast routing in media DC fabrics. iRP utilizes a controller-based architecture to optimize multicast tree formation and to increase bandwidth availability in the fabric. The system offers up to 50\% increase in fabric capacity to handle multicast flows passing through the fabric. In the second part of this dissertation, DiRP algorithm is presented. DiRP is based on a distributed decision-making approach to achieve multicast tree capacity optimization while maintaining low multicast tree setup time. DiRP algorithm is tested using commercially available data center switches. DiRP algorithm offers substantially lower path setup time compared to centralized systems while maintaining bandwidth awareness when setting up the fabric. The third part of this dissertation studies the utilization of machine learning algorithms to improve on multicast efficiency in the fabric. The work includes implementation and testing of LiRP algorithm to increase iRP\u27s fabric efficiency by implementing k-fold cross validation method to predict future multicast group memberships for time-series analysis. Testing results confirm that LiRP system increases the efficiency of iRP by up to 40\% through prediction of multicast group memberships with online arrival. In the fourth part of this dissertation, The problem of live video monitoring is studied. Existing network monitoring tools are either reactive by design or perform generic monitoring of flows with no insights into video domain. MediaFlow is a robust system for active network monitoring and reporting of video quality for thousands of flows simultaneously using a fraction of the cost of traditional monitoring solutions. MediaFlow is able to detect and report on integrity of video flows at a granularity of 100 mSec at line rate for thousands of flows. The system increases video monitoring scale by a thousand-fold compared to edge monitoring solutions