6 research outputs found
Adaptive Streaming in P2P Live Video Systems: A Distributed Rate Control Approach
Dynamic Adaptive Streaming over HTTP (DASH) is a recently proposed standard
that offers different versions of the same media content to adapt the delivery
process over the Internet to dynamic bandwidth fluctuations and different user
device capabilities. The peer-to-peer (P2P) paradigm for video streaming allows
to leverage the cooperation among peers, guaranteeing to serve every video
request with increased scalability and reduced cost. We propose to combine
these two approaches in a P2P-DASH architecture, exploiting the potentiality of
both. The new platform is made of several swarms, and a different DASH
representation is streamed within each of them; unlike client-server DASH
architectures, where each client autonomously selects which version to download
according to current network conditions and to its device resources, we put
forth a new rate control strategy implemented at peer site to maintain a good
viewing quality to the local user and to simultaneously guarantee the
successful operation of the P2P swarms. The effectiveness of the solution is
demonstrated through simulation and it indicates that the P2P-DASH platform is
able to warrant its users a very good performance, much more satisfying than in
a conventional P2P environment where DASH is not employed. Through a comparison
with a reference DASH system modeled via the Integer Linear Programming (ILP)
approach, the new system is shown to outperform such reference architecture. To
further validate the proposal, both in terms of robustness and scalability,
system behavior is investigated in the critical condition of a flash crowd,
showing that the strong upsurge of new users can be successfully revealed and
gradually accommodated.Comment: 12 pages, 17 figures, this work has been submitted to the IEEE
journal on selected Area in Communication
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