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

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

QoE-centric management of advanced multimedia services

Over the last years, multimedia content has become more prominent than ever. Particularly, video streaming is responsible for more than a half of the total global bandwidth consumption on the Internet. As the original Internet was not designed to deliver such real-time, bandwidth-consuming applications, a serious challenge is posed on how to efficiently provide the best service to the users. This requires a shift in the classical approach used to deliver multimedia content, from a pure Quality of Service (QoS) to a full Quality of Experience (QoE) perspective. While QoS parameters are mainly related to low-level network aspects, the QoE reflects how the end-users perceive a particular multimedia service. As the relationship between QoS parameters and QoE is far from linear, a classical QoS-centric delivery is not able to fully optimize the quality as perceived by the users. This paper provides an overview of the main challenges this PhD aims to tackle in the field of end-to-end QoE optimization of video streaming services and, more precisely, of HTTP Adaptive Streaming (HAS) solutions, which are quickly becoming the de facto standard for video delivery over the Internet