11,017 research outputs found
Evaluation of HTTP/DASH Adaptation Algorithms on Vehicular Networks
Video streaming currently accounts for the majority of Internet traffic. One
factor that enables video streaming is HTTP Adaptive Streaming (HAS), that
allows the users to stream video using a bit rate that closely matches the
available bandwidth from the server to the client. MPEG Dynamic Adaptive
Streaming over HTTP (DASH) is a widely used standard, that allows the clients
to select the resolution to download based on their own estimations. The
algorithm for determining the next segment in a DASH stream is not partof the
standard, but it is an important factor in the resulting playback quality.
Nowadays vehicles are increasingly equipped with mobile communication devices,
and in-vehicle multimedia entertainment systems. In this paper, we evaluate the
performance of various DASH adaptation algorithms over a vehicular network. We
present detailed simulation results highlighting the advantages and
disadvantages of various adaptation algorithms in delivering video content to
vehicular users, and we show how the different adaptation algorithms perform in
terms of throughput, playback interruption time, and number of interruptions
A Comparative Case Study of HTTP Adaptive Streaming Algorithms in Mobile Networks
HTTP Adaptive Streaming (HAS) techniques are now the dominant solution for
video delivery in mobile networks. Over the past few years, several HAS
algorithms have been introduced in order to improve user quality-of-experience
(QoE) by bit-rate adaptation. Their difference is mainly the required input
information, ranging from network characteristics to application-layer
parameters such as the playback buffer. Interestingly, despite the recent
outburst in scientific papers on the topic, a comprehensive comparative study
of the main algorithm classes is still missing. In this paper we provide such
comparison by evaluating the performance of the state-of-the-art HAS algorithms
per class, based on data from field measurements. We provide a systematic study
of the main QoE factors and the impact of the target buffer level. We conclude
that this target buffer level is a critical classifier for the studied HAS
algorithms. While buffer-based algorithms show superior QoE in most of the
cases, their performance may differ at the low target buffer levels of live
streaming services. Overall, we believe that our findings provide valuable
insight for the design and choice of HAS algorithms according to networks
conditions and service requirements.Comment: 6 page
Joint On-the-Fly Network Coding/Video Quality Adaptation for Real-Time Delivery
This paper introduces a redundancy adaptation algorithm for an on-the-fly
erasure network coding scheme called Tetrys in the context of real-time video
transmission. The algorithm exploits the relationship between the redundancy
ratio used by Tetrys and the gain or loss in encoding bit rate from changing a
video quality parameter called the Quantization Parameter (QP). Our evaluations
show that with equal or less bandwidth occupation, the video protected by
Tetrys with redundancy adaptation algorithm obtains a PSNR gain up to or more 4
dB compared to the video without Tetrys protection. We demonstrate that the
Tetrys redundancy adaptation algorithm performs well with the variations of
both loss pattern and delay induced by the networks. We also show that Tetrys
with the redundancy adaptation algorithm outperforms FEC with and without
redundancy adaptation
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
On the merits of SVC-based HTTP adaptive streaming
HTTP Adaptive Streaming (HAS) is quickly becoming the dominant type of video streaming in Over-The-Top multimedia services. HAS content is temporally segmented and each segment is offered in different video qualities to the client. It enables a video client to dynamically adapt the consumed video quality to match with the capabilities of the network and/or the client's device. As such, the use of HAS allows a service provider to offer video streaming over heterogeneous networks and to heterogeneous devices. Traditionally, the H. 264/AVC video codec is used for encoding the HAS content: for each offered video quality, a separate AVC video file is encoded. Obviously, this leads to a considerable storage redundancy at the video server as each video is available in a multitude of qualities. The recent Scalable Video Codec (SVC) extension of H. 264/AVC allows encoding a video into different quality layers: by dowloading one or more additional layers, the video quality can be improved. While this leads to an immediate reduction of required storage at the video server, the impact of using SVC-based HAS on the network and perceived quality by the user are less obvious. In this article, we characterize the performance of AVC- and SVC-based HAS in terms of perceived video quality, network load and client characteristics, with the goal of identifying advantages and disadvantages of both options
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