3 research outputs found
Measuring DASH Streaming Performance from the End Users Perspective using Neubot
The popularity of DASH streaming is rapidly increasing and a number of commercial streaming services are adopting this new standard. While the benefits of building streaming services on top of the HTTP protocol are clear, further work is still necessary to evaluate and enhance the system performance from the perspective of the end user. Here we present a novel framework to evaluate the performance of rate-adaptation algorithms for DASH streaming using network measurements collected from more than a thousand Internet clients. Data, which have been made publicly available, are collected by a DASH module built on top of Neubot, an open source tool for the collection of network measurements. Some examples about the possible usage of the collected data are given, ranging from simple analysis and performance comparisons of download speeds to the performance simulation of alternative adaptation strategies using, e.g., the instantaneous available bandwidth value
Improved DASH Architecture for Quality Cloud Video Streaming in Automated Systems
In modern times, multimedia streaming systems that transmit video across a channel primarily use HTTP services as a delivery component. Encoding the video for all quality levels is avoided thanks to fuzzy based encoders' ability to react to network changes. Additionally, the system frequently uses packet priority assignment utilising a linear error model to enhance the dynamic nature of DASH without buffering. Based on a fuzzy encoder, the decision of video quality is made in consideration of the bandwidth available. This is a component of the MPEG DASH encoder. The Fuzzy DASH system seeks to increase the scalability of online video streaming, making it suitable for live video broadcasts through mobile and other devices
Optimized Adaptive Streaming Representations based on System Dynamics
Adaptive streaming addresses the increasing and heterogenous demand of
multimedia content over the Internet by offering several encoded versions for
each video sequence. Each version (or representation) has a different
resolution and bit rate, aimed at a specific set of users, like TV or mobile
phone clients. While most existing works on adaptive streaming deal with
effective playout-control strategies at the client side, we take in this paper
a providers' perspective and propose solutions to improve user satisfaction by
optimizing the encoding rates of the video sequences. We formulate an integer
linear program that maximizes users' average satisfaction, taking into account
the network dynamics, the video content information, and the user population
characteristics. The solution of the optimization is a set of encoding
parameters that permit to create different streams to robustly satisfy users'
requests over time. We simulate multiple adaptive streaming sessions
characterized by realistic network connections models, where the proposed
solution outperforms commonly used vendor recommendations, in terms of user
satisfaction but also in terms of fairness and outage probability. The
simulation results further show that video content information as well as
network constraints and users' statistics play a crucial role in selecting
proper encoding parameters to provide fairness a mong users and to reduce
network resource usage. We finally propose a few practical guidelines that can
be used to choose the encoding parameters based on the user base
characteristics, the network capacity and the type of video content