9,255 research outputs found
Saving Energy in Mobile Devices for On-Demand Multimedia Streaming -- A Cross-Layer Approach
This paper proposes a novel energy-efficient multimedia delivery system
called EStreamer. First, we study the relationship between buffer size at the
client, burst-shaped TCP-based multimedia traffic, and energy consumption of
wireless network interfaces in smartphones. Based on the study, we design and
implement EStreamer for constant bit rate and rate-adaptive streaming.
EStreamer can improve battery lifetime by 3x, 1.5x and 2x while streaming over
Wi-Fi, 3G and 4G respectively.Comment: Accepted in ACM Transactions on Multimedia Computing, Communications
and Applications (ACM TOMCCAP), November 201
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
Q-AIMD: A Congestion Aware Video Quality Control Mechanism
Following the constant increase of the multimedia traffic, it seems necessary to allow transport protocols to be aware of the video quality of the transmitted flows rather than the throughput. This paper proposes a novel transport mechanism adapted to video flows. Our proposal, called Q-AIMD for video quality AIMD (Additive Increase Multiplicative Decrease), enables fairness in video quality while transmitting multiple video flows. Targeting video quality fairness allows improving the overall video quality for all transmitted flows, especially when the transmitted videos provide various types of content with different spatial resolutions. In addition, Q-AIMD mitigates the occurrence of network congestion events, and dissolves the congestion whenever it occurs by decreasing the video quality and hence the bitrate. Using different video quality metrics, Q-AIMD is evaluated with different video contents and spatial resolutions. Simulation results show that Q-AIMD allows an improved overall video quality among the multiple transmitted video flows compared to a throughput-based congestion control by decreasing significantly the quality discrepancy between them
The QUIC Fix for Optimal Video Streaming
Within a few years of its introduction, QUIC has gained traction: a
significant chunk of traffic is now delivered over QUIC. The networking
community is actively engaged in debating the fairness, performance, and
applicability of QUIC for various use cases, but these debates are centered
around a narrow, common theme: how does the new reliable transport built on top
of UDP fare in different scenarios? Support for unreliable delivery in QUIC
remains largely unexplored.
The option for delivering content unreliably, as in a best-effort model,
deserves the QUIC designers' and community's attention. We propose extending
QUIC to support unreliable streams and present a simple approach for
implementation. We discuss a simple use case of video streaming---an
application that dominates the overall Internet traffic---that can leverage the
unreliable streams and potentially bring immense benefits to network operators
and content providers. To this end, we present a prototype implementation that,
by using both the reliable and unreliable streams in QUIC, outperforms both TCP
and QUIC in our evaluations.Comment: Published to ACM CoNEXT Workshop on the Evolution, Performance, and
Interoperability of QUIC (EPIQ
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
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