856 research outputs found
NEWCAST: Anticipating Resource Management and QoE Provisioning for Mobile Video Streaming
The knowledge of future throughput variations in mobile networks becomes more
and more possible today thanks to the rich contextual information provided by
mobile applications and services and smartphone sensors. It is even likely that
such contextual information, which may include traffic, mobility and radio
conditions will lead to a novel agile resource management not yet thought of.
In this paper, we propose an framework (called NEWCAST) that anticipates the
throughput variations to deliver video streaming content. We develop an
optimization problem that realizes a fundamental trade-off among critical
metrics that impact the user's perceptual quality of experience (QoE) and the
cost of system utilization. Both simulated and real-world throughput traces
collected from [1], were carried out to evaluate the performance of NEWCAST. In
particular, we show from our numerical results that NEWCAST provides the
efficiency that the new 5G architectures require in terms of computational
complexity and robustness. We also implement a prototype system of NEWCAST and
evaluate it in a real environment with a real player to show its efficiency and
scalability compared to baseline adaptive bitrate algorithms.Comment: 14 pages, 22 figure
Backward-Shifted Strategies Based on SVC for HTTP Adaptive Video Streaming
Although HTTP-based video streaming can easily penetrate firewalls and profit
from Web caches, the underlying TCP may introduce large delays in case of a
sudden capacity loss. To avoid an interruption of the video stream in such
cases we propose the Backward-Shifted Coding (BSC). Based on Scalable Video
Coding (SVC), BSC adds a time-shifted layer of redundancy to the video stream
such that future frames are downloaded at any instant. This pre-fetched content
maintains a fluent video stream even under highly variant network conditions
and leads to high Quality of Experience (QoE). We characterize this QoE gain by
analyzing initial buffering time, re-buffering time and content resolution
using the Ballot theorem. The probability generating functions of the playback
interruption and of the initial buffering latency are provided in closed form.
We further compute the quasi-stationary distribution of the video quality, in
order to compute the average quality, as well as temporal variability in video
quality. Employing these analytic results to optimize QoE shows interesting
trade-offs and video streaming at outstanding fluency.Comment: 9 page
Tiyuntsong: A Self-Play Reinforcement Learning Approach for ABR Video Streaming
Existing reinforcement learning~(RL)-based adaptive bitrate~(ABR) approaches
outperform the previous fixed control rules based methods by improving the
Quality of Experience~(QoE) score, as the QoE metric can hardly provide clear
guidance for optimization, finally resulting in the unexpected strategies. In
this paper, we propose \emph{Tiyuntsong}, a self-play reinforcement learning
approach with generative adversarial network~(GAN)-based method for ABR video
streaming. Tiyuntsong learns strategies automatically by training two agents
who are competing against each other. Note that the competition results are
determined by a set of rules rather than a numerical QoE score that allows
clearer optimization objectives. Meanwhile, we propose GAN Enhancement Module
to extract hidden features from the past status for preserving the information
without the limitations of sequence lengths. Using testbed experiments, we show
that the utilization of GAN significantly improves the Tiyuntsong's
performance. By comparing the performance of ABRs, we observe that Tiyuntsong
also betters existing ABR algorithms in the underlying metrics.Comment: Published in ICME 201
Quality of Experience from Cache Hierarchies: Keep your low-bitrate close, and high-bitrate closer
Recent studies into streaming media delivery suggest that performance gains
from cache hierarchies such as Information-Centric Networks (ICNs) may be
negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieving
multimedia content. The bitrate adaptation mechanisms that drive video
streaming clash with caching hierarchies in ways that affect users' Quality of
Experience (QoE). Cache performance also diminishes as consumers dynamically
select content encoded at different bitrates. In this paper we use the evidence
to draw a novel insight: in a cache hierarchy for adaptive streaming content,
bitrates should be prioritized over or alongside popularity and hit rates. We
build on this insight to propose RippleCache as a family of cache placement
schemes that safeguard high-bitrate content at the edge and push low-bitrate
content into the network core. Doing so reduces contention of cache resources,
as well as congestion in the network. To validate RippleCache claims we
construct two separate implementations. We design RippleClassic as a benchmark
solution that optimizes content placement by maximizing a measure for cache
hierarchies shown to have high correlation with QoE. In addition, our
lighter-weight RippleFinder is then re-designed with distributed execution for
application in large-scale systems. RippleCache performance gains are
reinforced by evaluations in NS-3 against state-of-the-art baseline approaches,
using standard measures of QoE as defined by the DASH Industry Forum.
Measurements show that RippleClassic and RippleFinder deliver content that
suffers less oscillation and rebuffering, as well as the highest levels of
video quality, indicating overall improvements to QoE.Comment: submitted to IEEE/ACM Transactions on Networkin
Objective assessment of region of interest-aware adaptive multimedia streaming quality
Adaptive multimedia streaming relies on controlled
adjustment of content bitrate and consequent video quality variation in order to meet the bandwidth constraints of the communication
link used for content delivery to the end-user. The values of the easy to measure network-related Quality of Service metrics have no direct relationship with the way moving images are
perceived by the human viewer. Consequently variations in the video stream bitrate are not clearly linked to similar variation in the user perceived quality. This is especially true if some human visual system-based adaptation techniques are employed. As research has shown, there are certain image regions in each frame of a video sequence on which the users are more interested than in the others. This paper presents the Region of Interest-based Adaptive Scheme (ROIAS) which adjusts differently the regions within each frame of the streamed multimedia content based on the user interest in them. ROIAS is presented and discussed in terms of the adjustment algorithms employed and their impact on the human perceived video quality. Comparisons with existing approaches, including a constant quality adaptation scheme across the whole frame area, are performed employing two objective metrics which estimate user perceived video quality
Learning to Predict Streaming Video QoE: Distortions, Rebuffering and Memory
Mobile streaming video data accounts for a large and increasing percentage of
wireless network traffic. The available bandwidths of modern wireless networks
are often unstable, leading to difficulties in delivering smooth, high-quality
video. Streaming service providers such as Netflix and YouTube attempt to adapt
their systems to adjust in response to these bandwidth limitations by changing
the video bitrate or, failing that, allowing playback interruptions
(rebuffering). Being able to predict end user' quality of experience (QoE)
resulting from these adjustments could lead to perceptually-driven network
resource allocation strategies that would deliver streaming content of higher
quality to clients, while being cost effective for providers. Existing
objective QoE models only consider the effects on user QoE of video quality
changes or playback interruptions. For streaming applications, adaptive network
strategies may involve a combination of dynamic bitrate allocation along with
playback interruptions when the available bandwidth reaches a very low value.
Towards effectively predicting user QoE, we propose Video Assessment of
TemporaL Artifacts and Stalls (Video ATLAS): a machine learning framework where
we combine a number of QoE-related features, including objective quality
features, rebuffering-aware features and memory-driven features to make QoE
predictions. We evaluated our learning-based QoE prediction model on the
recently designed LIVE-Netflix Video QoE Database which consists of practical
playout patterns, where the videos are afflicted by both quality changes and
rebuffering events, and found that it provides improved performance over
state-of-the-art video quality metrics while generalizing well on different
datasets. The proposed algorithm is made publicly available at
http://live.ece.utexas.edu/research/Quality/VideoATLAS release_v2.rar.Comment: under review in Transactions on Image Processin
Cloud Gaming With Foveated Graphics
Cloud gaming enables playing high end games, originally designed for PC or
game console setups, on low end devices, such as net-books and smartphones, by
offloading graphics rendering to GPU powered cloud servers. However,
transmitting the high end graphics requires a large amount of available network
bandwidth, even though it is a compressed video stream. Foveated video encoding
(FVE) reduces the bandwidth requirement by taking advantage of the non-uniform
acuity of human visual system and by knowing where the user is looking. We have
designed and implemented a system for cloud gaming with foveated graphics using
a consumer grade real-time eye tracker and an open source cloud gaming
platform. In this article, we describe the system and its evaluation through
measurements with representative games from different genres to understand the
effect of parameterization of the FVE scheme on bandwidth requirements and to
understand its feasibility from the latency perspective. We also present
results from a user study. The results suggest that it is possible to find a
"sweet spot" for the encoding parameters so that the users hardly notice the
presence of foveated encoding but at the same time the scheme yields most of
the bandwidth savings achievable.Comment: Submitted for publication in ACM TOM
Estimation of optimal encoding ladders for tiled 360{\deg} VR video in adaptive streaming systems
Given the significant industrial growth of demand for virtual reality (VR),
360{\deg} video streaming is one of the most important VR applications that
require cost-optimal solutions to achieve widespread proliferation of VR
technology. Because of its inherent variability of data-intensive content types
and its tiled-based encoding and streaming, 360{\deg} video requires new
encoding ladders in adaptive streaming systems to achieve cost-optimal and
immersive streaming experiences. In this context, this paper targets both the
provider's and client's perspectives and introduces a new content-aware
encoding ladder estimation method for tiled 360{\deg} VR video in adaptive
streaming systems. The proposed method first categories a given 360{\deg} video
using its features of encoding complexity and estimates the visual distortion
and resource cost of each bitrate level based on the proposed distortion and
resource cost models. An optimal encoding ladder is then formed using the
proposed integer linear programming (ILP) algorithm by considering practical
constraints. Experimental results of the proposed method are compared with the
recommended encoding ladders of professional streaming service providers.
Evaluations show that the proposed encoding ladders deliver better results
compared to the recommended encoding ladders in terms of objective quality for
360{\deg} video, providing optimal encoding ladders using a set of service
provider's constraint parameters.Comment: The 19th IEEE International Symposium on Multimedia (ISM 2017),
Taichung, Taiwa
On the impact of video stalling and video quality in the case of camera switching during adaptive streaming of sports content
The widespread usage of second screens, in combination with mobile video streaming technologies like HTTP Adaptive Streaming (HAS), enable new means for taking end-users' Quality of Experience (QoE) to the next level. For sports events, these technological evolutions can, for example, enhance the overall engagement of remote fans or give them more control over the content. In this paper, we consider the case of adaptively streaming multi-camera sports content to tablet devices, enabling the end-user to dynamically switch cameras. Our goal is to subjectively evaluate the trade-off between video stalling duration (as a result of requesting another camera feed) and initial video quality of the new feed. Our results show that short video stallings do not significantly influence overall quality ratings, that quality perception is highly influenced by the video quality at the moment of camera switching and that large quality fluctuations should be avoided
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