38,035 research outputs found
Streaming Video over HTTP with Consistent Quality
In conventional HTTP-based adaptive streaming (HAS), a video source is
encoded at multiple levels of constant bitrate representations, and a client
makes its representation selections according to the measured network
bandwidth. While greatly simplifying adaptation to the varying network
conditions, this strategy is not the best for optimizing the video quality
experienced by end users. Quality fluctuation can be reduced if the natural
variability of video content is taken into consideration. In this work, we
study the design of a client rate adaptation algorithm to yield consistent
video quality. We assume that clients have visibility into incoming video
within a finite horizon. We also take advantage of the client-side video
buffer, by using it as a breathing room for not only network bandwidth
variability, but also video bitrate variability. The challenge, however, lies
in how to balance these two variabilities to yield consistent video quality
without risking a buffer underrun. We propose an optimization solution that
uses an online algorithm to adapt the video bitrate step-by-step, while
applying dynamic programming at each step. We incorporate our solution into
PANDA -- a practical rate adaptation algorithm designed for HAS deployment at
scale.Comment: Refined version submitted to ACM Multimedia Systems Conference
(MMSys), 201
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
Region of interest-based adaptive multimedia streaming scheme
Adaptive multimedia streaming aims at adjusting
the transmitted content based on the available bandwidth such as losses that often severely affect the end-user perceived quality are minimized and consequently the transmission quality increases. Current solutions affect equally the whole viewing area of the multimedia frames, despite research showing that there are regions on which the viewers are more interested in than on others. This paper presents a novel region of interest-based adaptive scheme (ROIAS) for multimedia streaming that when performing transmission-related quality adjustments, selectively affects the quality of those regions of the image the viewers are the least interested in. As the quality of the regions the viewers are the most interested in will not change (or will involve little change),the proposed scheme provides higher overall end-user perceived
quality than any of the existing adaptive solutions
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
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