Resilience of Video Streaming Services to Network Impairments

When dealing with networks, performance management through conventional quality of service (QoS)-based methods becomes difficult and is often ineffective. In fact, quality emerges as an end-to-end factor, for it is particularly sensitive to the end-user perception of the overall service, i.e., the user's quality of experience (QoE). However, the two are not independent from each other and their relationship has to be studied through metrics that go beyond the typical network parameters. To better explore the value of assessing QoE alongside QoS in high-speed, lossy networks, this paper presents an experimental methodology to understand the relation between network QoS onto service QoE, with the aim to perform a combined network-service assessment. Using video streaming services as the test-case (given their extended usage nowadays), in this paper, we provide studies on three network-impaired video-sets with the aim to provide a comprehensive evaluation of the effects of networks on video quality. First, the ReTRIeVED video set provides the means to understand the most impairing effects on networks. Furthermore, it triggered the idea to create our own sets, specialized in the most impairing conditions for 2-D and 3-D: the LIMP Video Quality Database and the 3-D-HEVC-Net Video Quality Database. Our study and methodology are meant to provide service providers with the means to pinpoint the working boundaries of their video-sets in face of different network conditions. At the same time, network operators may use our findings to predict how network control policies affect the user's perception of the service

Digital Holography Data Compression

Digital holography processing is a research topic related to the development of novel visual immersive applications. The huge amount of information conveyed by a digital hologram and the different properties of holographic data with respect to conventional photographic data require a comprehension of the performances and limitations of current image and video standard techniques. This paper proposes an architecture for objective evaluation of the performances of the state-of-the-art compression techniques applied to digital holographic data

A Model for Mapping Combined Effects of Quality of Service Parameters and Device Features on Video Streaming Quality of Experience

Maintaining quality of streaming video is challenged by network faults resulting into freezes and rebufferings on the video. On top of the network effects, device features have impacts on the image of the video frames displayed during streaming. Despite the simultaneous impacts of video quality from network and device, previous studies considered individual impact of network parameters or devices as influencing factors to propose Quality of Experience (QoE) models. This study proposed QoE model by mapping combined effects from both network and device on video streamed QoE. An experiment to study the effects of video quality from combined effects of network and device over the wireless involved 35 subjects. Combination of packet loss, packet reordering, and delay were emulated using network emulator following Design of Experiment methodology. Through analysis of variance, the study found that packet loss had the highest impact, followed by device features, reordering, and delay on the video QoE. From the combined effects, two-way interactions and three-way interactions had significant effects on the video QoE. Through additive and linearity behavior of the input factors from network and device on video streaming QoE, a multi-factor model was derived. Keywords: Design of Experiment (DOE); Mean Opinion Score (MOS); Quality of Experience (QoE); Quality of Service (QoS); Video Quality Assessmen

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

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques