1,716 research outputs found
Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach
HTTP based adaptive video streaming has become a popular choice of streaming
due to the reliable transmission and the flexibility offered to adapt to
varying network conditions. However, due to rate adaptation in adaptive
streaming, the quality of the videos at the client keeps varying with time
depending on the end-to-end network conditions. Further, varying network
conditions can lead to the video client running out of playback content
resulting in rebuffering events. These factors affect the user satisfaction and
cause degradation of the user quality of experience (QoE). It is important to
quantify the perceptual QoE of the streaming video users and monitor the same
in a continuous manner so that the QoE degradation can be minimized. However,
the continuous evaluation of QoE is challenging as it is determined by complex
dynamic interactions among the QoE influencing factors. Towards this end, we
present LSTM-QoE, a recurrent neural network based QoE prediction model using a
Long Short-Term Memory (LSTM) network. The LSTM-QoE is a network of cascaded
LSTM blocks to capture the nonlinearities and the complex temporal dependencies
involved in the time varying QoE. Based on an evaluation over several publicly
available continuous QoE databases, we demonstrate that the LSTM-QoE has the
capability to model the QoE dynamics effectively. We compare the proposed model
with the state-of-the-art QoE prediction models and show that it provides
superior performance across these databases. Further, we discuss the state
space perspective for the LSTM-QoE and show the efficacy of the state space
modeling approaches for QoE prediction
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
Predictive CDN Selection for Video Delivery Based on LSTM Network Performance Forecasts and Cost-Effective Trade-Offs
Owing to increasing consumption of video streams and demand for higher quality content and more advanced displays, future telecommunication networks are expected to outperform current networks in terms of key performance indicators (KPIs). Currently, content delivery networks (CDNs) are used to enhance media availability and delivery performance across the Internet in a cost-effective manner. The proliferation of CDN vendors and business models allows the content provider (CP) to use multiple CDN providers simultaneously. However, extreme concurrency dynamics can affect CDN capacity, causing performance degradation and outages, while overestimated demand affects costs. 5G standardization communities envision advanced network functions executing video analytics to enhance or boost media services. Network accelerators are required to enforce CDN resilience and efficient utilization of CDN assets. In this regard, this study investigates a cost-effective service to dynamically select the CDN for each session and video segment at the Media Server, without any modification to the video streaming pipeline being required. This service performs time series forecasts by employing a Long Short-Term Memory (LSTM) network to process real time measurements coming from connected video players. This service also ensures reliable and cost-effective content delivery through proactive selection of the CDN that fits with performance and business constraints. To this end, the proposed service predicts the number of players that can be served by each CDN at each time; then, it switches the required players between CDNs to keep the (Quality of Service) QoS rates or to reduce the CP's operational expenditure (OPEX). The proposed solution is evaluated by a real server, CDNs, and players and delivering dynamic adaptive streaming over HTTP (MPEG-DASH), where clients are notified to switch to another CDN through a standard MPEG-DASH media presentation description (MPD) update mechanismThis work was supported in part by the EC projects Fed4Fire+, under Grant 732638 (H2020-ICT-13-2016, Research and Innovation Action), and in part by Open-VERSO project (Red Cervera Program, Spanish Government's Centre for the Development of Industrial Technology
MANSY: Generalizing Neural Adaptive Immersive Video Streaming With Ensemble and Representation Learning
The popularity of immersive videos has prompted extensive research into
neural adaptive tile-based streaming to optimize video transmission over
networks with limited bandwidth. However, the diversity of users' viewing
patterns and Quality of Experience (QoE) preferences has not been fully
addressed yet by existing neural adaptive approaches for viewport prediction
and bitrate selection. Their performance can significantly deteriorate when
users' actual viewing patterns and QoE preferences differ considerably from
those observed during the training phase, resulting in poor generalization. In
this paper, we propose MANSY, a novel streaming system that embraces user
diversity to improve generalization. Specifically, to accommodate users'
diverse viewing patterns, we design a Transformer-based viewport prediction
model with an efficient multi-viewport trajectory input output architecture
based on implicit ensemble learning. Besides, we for the first time combine the
advanced representation learning and deep reinforcement learning to train the
bitrate selection model to maximize diverse QoE objectives, enabling the model
to generalize across users with diverse preferences. Extensive experiments
demonstrate that MANSY outperforms state-of-the-art approaches in viewport
prediction accuracy and QoE improvement on both trained and unseen viewing
patterns and QoE preferences, achieving better generalization.Comment: This work has been submitted to the IEEE Transactions on Mobile
Computing for possible publication. Copyright may be transferred without
notice, after which this version may no longer be accessibl
Bitrate Ladder Prediction Methods for Adaptive Video Streaming: A Review and Benchmark
HTTP adaptive streaming (HAS) has emerged as a widely adopted approach for
over-the-top (OTT) video streaming services, due to its ability to deliver a
seamless streaming experience. A key component of HAS is the bitrate ladder,
which provides the encoding parameters (e.g., bitrate-resolution pairs) to
encode the source video. The representations in the bitrate ladder allow the
client's player to dynamically adjust the quality of the video stream based on
network conditions by selecting the most appropriate representation from the
bitrate ladder. The most straightforward and lowest complexity approach
involves using a fixed bitrate ladder for all videos, consisting of
pre-determined bitrate-resolution pairs known as one-size-fits-all. Conversely,
the most reliable technique relies on intensively encoding all resolutions over
a wide range of bitrates to build the convex hull, thereby optimizing the
bitrate ladder for each specific video. Several techniques have been proposed
to predict content-based ladders without performing a costly exhaustive search
encoding. This paper provides a comprehensive review of various methods,
including both conventional and learning-based approaches. Furthermore, we
conduct a benchmark study focusing exclusively on various learning-based
approaches for predicting content-optimized bitrate ladders across multiple
codec settings. The considered methods are evaluated on our proposed
large-scale dataset, which includes 300 UHD video shots encoded with software
and hardware encoders using three state-of-the-art encoders, including
AVC/H.264, HEVC/H.265, and VVC/H.266, at various bitrate points. Our analysis
provides baseline methods and insights, which will be valuable for future
research in the field of bitrate ladder prediction. The source code of the
proposed benchmark and the dataset will be made publicly available upon
acceptance of the paper
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