82 research outputs found
Design and evaluation of a DASH-compliant second screen video player for live events in mobile scenarios
The huge diffusion of mobile devices is rapidly changing the way multimedia content is consumed. Mobile devices are often used as a second screen, providing complementary information on the content shown on the primary screen, as different camera angles in case of a sport event. The introduction of multiple camera angles poses many challenges with respect to guaranteeing a high Quality of Experience to the end user, especially when the live aspect, different devices and highly variable network conditions typical of mobile environments come into play. Due to the ability of HTTP Adaptive Streaming (HAS) protocols to dynamically adapt to bandwidth fluctuations, they are especially suited for the delivery of multimedia content in mobile environments. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. Recently, a standardized solution has been proposed by the MPEG consortium, called Dynamic Adaptive Streaming over HTTP (DASH). We present in this paper a DASH-compliant iOS video player designed to support research on rate adaptation heuristics for live second screen scenarios in mobile environments. The video player allows to monitor the battery consumption and CPU usage of the mobile device and to provide this information to the heuristic. Live and Video-on-Demand streaming scenarios and real-time multi-video switching are supported as well. Quantitative results based on real 3G traces are reported on how the developed prototype has been used to benchmark two existing heuristics and to analyse the main aspects affecting battery lifetime in mobile video streaming
Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations
Video streaming is predicted to become the dominating traffic in mobile broadband networks. At
the same time, adaptive HTTP streaming is developing into the preferred way of streaming media
over the Internet. In this paper, we evaluate how different components of a streaming system can be
optimized when serving content to mobile devices in particular. We first analyze the media traffic from
a Norwegian network and media provider. Based on our findings, we outline benefits and challenges for
HTTP streaming, on the sender and the receiver side, and we investigate how HTTP-based streaming
affects server performance. Furthermore, we discuss various aspects of efficient coding of the video
segments from both performance and user perception point of view. The final part of the paper
studies efficient adaptation and delivery to mobile devices over wireless networks. We experimentally
evaluate and improve adaptation strategies, multilink solutions, and bandwidth prediction techniques. Based on the results from our evaluations, we make recommendations for how an adaptive streaming
system should handle mobile devices. Small changes, or simple awareness of how users perceive quality,
can often have large effects
Quality of experience-centric management of adaptive video streaming services : status and challenges
Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years
Design and optimisation of a (FA)Q-learning-based HTTP adaptive streaming client
In recent years, HTTP (Hypertext Transfer Protocol) adaptive streaming (HAS) has become the de facto standard for adaptive video streaming services. A HAS video consists of multiple segments, encoded at multiple quality levels. State-of-the-art HAS clients employ deterministic heuristics to dynamically adapt the requested quality level based on the perceived network conditions. Current HAS client heuristics are, however, hardwired to fit specific network configurations, making them less flexible to fit a vast range of settings. In this article, a (frequency adjusted) Q-learning HAS client is proposed. In contrast to existing heuristics, the proposed HAS client dynamically learns the optimal behaviour corresponding to the current network environment in order to optimise the quality of experience. Furthermore, the client has been optimised both in terms of global performance and convergence speed. Thorough evaluations show that the proposed client can outperform deterministic algorithms by 11-18% in terms of mean opinion score in a wide range of network configurations
A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques
A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks
Prediction-based techniques for the optimization of mobile networks
Mención Internacional en el título de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition
of several random phenomena, most of which, related to human activities, such as mobility,
communications and network usage. However, when observed in their totality, the many individual
components merge into more deterministic patterns and trends start to be identifiable and
predictable.
In this thesis we analyze a recent branch of network optimization that is commonly referred to
as anticipatory networking and that entails the combination of prediction solutions and network
optimization schemes. The main intuition behind anticipatory networking is that knowing in
advance what is going on in the network can help understanding potentially severe problems and
mitigate their impact by applying solution when they are still in their initial states. Conversely,
network forecast might also indicate a future improvement in the overall network condition (i.e.
load reduction or better signal quality reported from users). In such a case, resources can be
assigned more sparingly requiring users to rely on buffered information while waiting for the
better condition when it will be more convenient to grant more resources.
In the beginning of this thesis we will survey the current anticipatory networking panorama
and the many prediction and optimization solutions proposed so far. In the main body of the work,
we will propose our novel solutions to the problem, the tools and methodologies we designed to
evaluate them and to perform a real world evaluation of our schemes.
By the end of this work it will be clear that not only is anticipatory networking a very promising
theoretical framework, but also that it is feasible and it can deliver substantial benefit to current
and next generation mobile networks. In fact, with both our theoretical and practical results we
show evidences that more than one third of the resources can be saved and even larger gain can
be achieved for data rate enhancements.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge Ortín Gracia.- Vocal: Guevara Noubi
The Transmission and Processing of Sensor-rich Videos in Mobile Environment
Ph.DDOCTOR OF PHILOSOPH
Proxy Support for HTTP Adaptive Streaming
Not long ago streaming video over the Internet included only short clips of low quality video. Now the possibilities seem endless as professional productions are made available in high definition. This explosion of growth is the result of several factors, such as increasing network performance, advancements in video encoding technology, improvements to video streaming techniques, and a growing number of devices capable of handling video. However, despite the improvements to Internet video streaming this paradigm is still evolving.
HTTP adaptive streaming involves encoding a video at multiple quality levels then dividing those quality levels into small chunks. The player can then determine which quality level to retrieve the next chunk from in order to optimize video playback when considering the underlying network conditions. This thesis first presents an experimental framework that allows for adaptive streaming players to be analyzed and evaluated. Evaluation is beneficial because there are several concerns with the adaptive video streaming ecosystem such as achieving a high video playback quality while also ensuring stable playback quality.
The primary contribution of this thesis is the evaluation of prefetching by a proxy server as a means to improve streaming performance. This work considers an implementation of a proxy server that is functional with the extremely popular Netflix streaming service, and it is evaluated using two Netflix players. The results show its potential to improve video streaming performance in several scenarios. It effectively increases the buffer capacity of the player as chunks can be prefetched in advance of the player's request then stored on the proxy to be quickly delivered once requested. This allows for degradation in network conditions to be hidden from the player while the proxy serves prefetched data, preventing a reduction to the video quality as a result of an overreaction by the player. Further, the proxy can reduce the impact of the bottleneck in the network, achieving higher throughput by utilizing parallel connections to the server
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