Improving Perceived Fairness and QoE for Adaptive Video Streams
The growth of broadband Internet access across the world for the last 10 years has made new business models possible. Services which before mainly were provided by network operators and in dedicated networks have now migrated to the open Internet. This has created a global service provider market, using the Internet as platform. The network operators are in many cases left with only providing the broadband access service. For the global service providers, continuous effort is put into the field of finding smart methods for bringing new and advanced services to the market without asking for e.g. QoS features from the involved network operators. This type of service delivery is called Over The Top (OTT) The concept of making services able to adapt their network and transport requirements during time of delivery is a strong contribution to success for OTT services.The focus of the work in this thesis, is methods for improving various aspects as defined by my research questions related to QoS and potentially also QoE for dynamic adaptive video streaming over HTTP (DASH) services. The motivation for focusing on video services is based on their high QoS requirements (i.e. bandwidth) and also popularity in terms of usage.In the work presented in this thesis, I have studied the behaviour and performance of DASH services by means of simulations, measurements and experiments. Based on insight obtained through this, I established a hypothesis on how QoS and QoE aspects could be improved for users present in the same home network environment. My hypothesis was that making more accurate information available about both services and network conditions in near real time could facilitate improved control methods. The effectiveness of my suggested methods have in most cases been analysed by means of implementation in an experimental lab scenario, and supported by simulations and analytical approaches when appropriate.The findings presented in the included papers are all closely related and map into the research model used. This model is composed of Knowledge Plane, Monitor Plane and Action plane components located in both service endpoints and involved network components. As my main focus has been on OTT service delivery, the main contributions of my work apply to service endpoints, i.e. components in the home network and on the server side. The server side would in many cases be represented by a Content Delivery Network (CDN) node.The main research questions identified are:RQ1: In a home network how to (autonomously) control the performance of DASH based services.RQ2: How to provide fairness and stability for competing DASH sessions using service endpoint functionality.RQ3: How to choose appropriate quality levels for a DASH session.The main contributions from my research are:C1: A method for controlling the quality levels of DASH in the home gateway.C2: A method for improving fairness among competing DASH sessions.C3: A method for shaping traffic aggregates on access links with DASH components.C4: A method for estimating available bandwidth on access links when DASH sessions are present.
ICNS 2012