4,547 research outputs found
QoE-Based Low-Delay Live Streaming Using Throughput Predictions
Recently, HTTP-based adaptive streaming has become the de facto standard for
video streaming over the Internet. It allows clients to dynamically adapt media
characteristics to network conditions in order to ensure a high quality of
experience, that is, minimize playback interruptions, while maximizing video
quality at a reasonable level of quality changes. In the case of live
streaming, this task becomes particularly challenging due to the latency
constraints. The challenge further increases if a client uses a wireless
network, where the throughput is subject to considerable fluctuations.
Consequently, live streams often exhibit latencies of up to 30 seconds. In the
present work, we introduce an adaptation algorithm for HTTP-based live
streaming called LOLYPOP (Low-Latency Prediction-Based Adaptation) that is
designed to operate with a transport latency of few seconds. To reach this
goal, LOLYPOP leverages TCP throughput predictions on multiple time scales,
from 1 to 10 seconds, along with an estimate of the prediction error
distribution. In addition to satisfying the latency constraint, the algorithm
heuristically maximizes the quality of experience by maximizing the average
video quality as a function of the number of skipped segments and quality
transitions. In order to select an efficient prediction method, we studied the
performance of several time series prediction methods in IEEE 802.11 wireless
access networks. We evaluated LOLYPOP under a large set of experimental
conditions limiting the transport latency to 3 seconds, against a
state-of-the-art adaptation algorithm from the literature, called FESTIVE. We
observed that the average video quality is by up to a factor of 3 higher than
with FESTIVE. We also observed that LOLYPOP is able to reach a broader region
in the quality of experience space, and thus it is better adjustable to the
user profile or service provider requirements.Comment: Technical Report TKN-16-001, Telecommunication Networks Group,
Technische Universitaet Berlin. This TR updated TR TKN-15-00
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
QoE-Aware Resource Allocation For Crowdsourced Live Streaming: A Machine Learning Approach
In the last decade, empowered by the technological advancements of mobile devices
and the revolution of wireless mobile network access, the world has witnessed an
explosion in crowdsourced live streaming. Ensuring a stable high-quality playback
experience is compulsory to maximize the viewers’ Quality of Experience and the
content providers’ profits. This can be achieved by advocating a geo-distributed cloud
infrastructure to allocate the multimedia resources as close as possible to viewers, in
order to minimize the access delay and video stalls.
Additionally, because of the instability of network condition and the heterogeneity of
the end-users capabilities, transcoding the original video into multiple bitrates is
required. Video transcoding is a computationally expensive process, where generally a
single cloud instance needs to be reserved to produce one single video bitrate
representation. On demand renting of resources or inadequate resources reservation
may cause delay of the video playback or serving the viewers with a lower quality. On
the other hand, if resources provisioning is much higher than the required, the
extra resources will be wasted.
In this thesis, we introduce a prediction-driven resource allocation framework, to
maximize the QoE of viewers and minimize the resources allocation cost. First, by
exploiting the viewers’ locations available in our unique dataset, we implement a machine learning model to predict the viewers’ number near each geo-distributed cloud
site. Second, based on the predicted results that showed to be close to the actual values,
we formulate an optimization problem to proactively allocate resources at the viewers’
proximity. Additionally, we will present a trade-off between the video access delay and
the cost of resource allocation.
Considering the complexity and infeasibility of our offline optimization to respond to
the volume of viewing requests in real-time, we further extend our work, by introducing
a resources forecasting and reservation framework for geo-distributed cloud sites. First,
we formulate an offline optimization problem to allocate transcoding resources at the
viewers’ proximity, while creating a tradeoff between the network cost and viewers
QoE. Second, based on the optimizer resource allocation decisions on historical live
videos, we create our time series datasets containing historical records of the optimal
resources needed at each geo-distributed cloud site. Finally, we adopt machine learning
to build our distributed time series forecasting models to proactively forecast the exact
needed transcoding resources ahead of time at each geo-distributed cloud site.
The results showed that the predicted number of transcoding resources needed in each
cloud site is close to the optimal number of transcoding resources
Measurement And Improvement of Quality-of-Experience For Online Video Streaming Services
Title from PDF of title page, viewed on September 4, 2015Dissertation advisor: Deep MedhiVitaIncludes bibliographic references (pages 126-141)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015HTTP based online video streaming services have been consistently dominating
the online traffic for the past few years. Measuring and improving the performance of
these services is an important challenge. Traditional Quality-of-Service (QoS) metrics
such as packet loss, jitter and delay which were used for networked services are not easily
understood by the users. Instead, Quality-of-Experience (QoE) metrics which capture the
overall satisfaction are more suitable for measuring the quality as perceived by the users.
However, these QoE metrics have not yet been standardized and their measurement and
improvement poses unique challenges. In this work we first present a comprehensive
survey of the different set of QoE metrics and the measurement methodologies suitable
for HTTP based online video streaming services.
We then present our active QoE measurement tool Pytomo that measures the QoE
of YouTube videos. A case study on the measurement of QoE of YouTube videos when
accessed by residential users from three different Internet Service Providers (ISP) in a
metropolitan area is discussed. This is the first work that has collected QoE data from
actual residential users using active measurements for YouTube videos. Based on these
measurements we were able to study and compare the QoE of YouTube videos across
multiple ISPs. We also were able to correlate the QoE observed with the server clusters
used for the different users. Based on this correlation we were able to identify the server
clusters that were experiencing diminished QoE.
DynamicAdaptive Streaming overHTTP (DASH) is an HTTP based video streaming
that enables the video players to adapt the video quality based on the network conditions.
We next present a rate adaptation algorithm that improves the QoE of DASH
video streaming services that selects the most optimum video quality. With DASH the
video server hosts multiple representation of the same video and each representation is
divided into small segments of constant playback duration. The DASH player downloads
the appropriate representation based on the network conditions, thus, adapting the video
quality to match the conditions. Currently deployed Adaptive Bitrate (ABR) algorithms
use throughput and buffer occupancy to predict segment fetch times. These algorithms
assume that the segments are of equal size. However, due to the encoding schemes employed
this assumption does not hold. In order to overcome these limitations, we propose
a novel Segment Aware Rate Adaptation algorithm (SARA) that leverages the knowledge
of the segment size variations to improve the prediction of segment fetch times. Using
an emulated player in a geographically distributed virtual network setup, we compare the
performance of SARA with existing ABR algorithms. We demonstrate that SARA helps
to improve the QoE of the DASH video streaming with improved convergence time, better
bitrate switching performance and better video quality. We also show that unlike the existing
adaptation schemes, SARA provides a consistent QoE irrespective of the segment
size distributions.Introduction -- Measurement of QoE for Online Video Streaming Services: A Literature Survey -- Pytomo: A Tool for measuring QoE of YouTube Videos -- Case Study: QoE across three Internet Service Providers in a Metropolitan Area -- Adaptive Bitrate Algorithms for DASH -- Segment Aware Rate Adaptation for DASH -- Performance Evaluation of SARA -- Conclusion and Future Research --Appendix A. Sample MPD Fil
Cognitive Video Streaming
Video-on-demand (VoD) streaming services are becoming increasingly popular due to their flexibility to allow users to access their favorite video contents anytime, anywhere from a wide range of access devices such as smart phones, computers and TV. The content providers rely on highly satisfied subscribers for revenue generation and there has been significant efforts in developing approaches to “estimate” the quality of experience (QoE) of VoD subscribers. But a key issue is that QoE is not defined, appropriate proxies needs to be found for QoE, via the streaming metrics (the quality of service (QoS) metrics) that are largely based on initial startup time, buffering delays, average bit rate and average throughput and other relevant factors such as the video content and user behavior and other external factors. The ultimate objective of the content provider is to elevate the QoE of all the subscribers at the cost of minimal network resources, such as hardware resources and bandwidth.
We propose a cognitive video streaming strategy in order to ensure the QoE of subscribers while utilizing minimal network resources. The proposed cognitive video streaming architecture consists of an estimation module, a prediction module and an adaptation module. Then, we demonstrate the prediction module of the cognitive video streaming architecture through a play time prediction tool. For this purpose, the applicability of different machine learning algorithms such as k-nearest neighbor, neural network regression and survival models are experimented with; then, we develop an approach to identify the most relevant factors that contributed to the prediction. The proposed approaches are tested on data set provided by Comcast Cable
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