Initially, video streaming protocol required downloading complete video before it could be played. This technology became surpassed by progressive download which allowed buffering a few seconds of the video hence allowing playback to start only after a sufficient fragment of the video was downloaded. These techniques were found to be ineffective during network congestion due to frame freezes and shuttered playback. This saw the evolution if HTTP-based streaming techniques which tackled these shortcomings by adapting the video to the current network conditions.
The HTTP based streaming has witnessed reduced playback interruption and high bandwidth utilization and therefore resulted to improved Quality of Experience (QoE). QoE refers to the the overall acceptability of the video streaming service, as perceived subjectively by the end- user. However, the end users of the HTTP based streaming have different internet speed connections and types. The dynamic adaptation to variable network and environment conditions is managed and controlled by the client. Not only does the server play a passive role but also the underlying network provide to active assistance to toward ensuring a certain level of QoE of streams to the end user . This presents a problem if the users are going to view a video with the same rate of transfer as well as quality. Having systems that adapt to the viewers connection quality and speed would ensure that the client obtains the highest quality possible. Addressing this problem via HTTP adaptive streaming will provide insight into the method employed to improve QoE.
Background and Size of the ProblemThe last decade has witnessed immense increase in video internet traffic. The ease of access to video services from almost gadget and location has attributed to the exploding connected consumers . In the light of this context, video streaming over the HTTP has become the most commanding approach in delivering multimedia content over the internet. Over the years, there have been a number of streaming protocol that evolved from datagram streaming to progressive download streaming and finally to adaptive HTTP streaming. Of significance is HTTP adaptive streaming (HAS) which has expeditiously emerged and taken a firm stand in the internet video delivery.
Several strategies have been employed to implement HAS by various stakeholders. These adaptive technologies share critical aspects which include the ability to segment media content into small chunks encoded at multiple bitrates. These HAS technologies have to ensured efficient delivery of multimedia contents by leveraging the existing internet infrastructure, such as HTTP content delivery network (CDN) and clientdriven nature of streaming content . During the playback, client uses its bitrate adaptation logic to dynamically fetch the chunk encoded at the optimal bitrate level based on clients environment and capability characteristics (e.g., client decoding and rendering capabilities, and available bandwidth of the network connection) .
In todays world, HAS have been overwhelmingly been adopted by the industry by players such a Netflix, You Tube, Hulu and ESPN sport as a major streaming solutions to stream videos to their ever growing users. It has also become convenient for a user to watch TV online, follow up on a live Olympic games or watching a live soccer match. While there are some strategies that have been employed to cope with varying delivery conditions under some specific challenging circumstances, HAS implementations has suffered from instability, unfairness and bandwidth underutilization. However, different strategies have been employed to provide a lasting solution to some of these problems. The solutions provided have ensured client with different internet speeds can view videos at their optimum speed and quality.
The use of HTTP adaptive streaming to improve QoE has increasingly become an important factor in the provision of high quality videos over HTTP servers . There has been no clear guideline on determining performance of QoE to the end user as QoE is subjective in nature. This brings another challenge when it comes to streaming the videos using mobile devices which are heterogeneous and their bandwidth fluctuates more often than in fixed network. As it will be illustrated in Chapter 2, research related to improving QoE has primarily been concentrated on technical aspects (such as optimizing bandwidth optimization) and to some extent on QoE impact on adaptation parameters.
The increasing number of mobile devices users implies an increase in consumer demand for multimedia traffic. The purpose of this dissertation is therefore to develop a system that adapt to the viewers connection quality and speed on mobile devices with an aim of improving QoE. The paper will also delve into the individual aspects of the suggested solutions. This will help in providing a wider scope of the details that would be necessary in providing the solutions required.
Important of this DissertationHand-held devices such as tablets and smartphones have become a major video streaming media. For example, YouTube  reports that half of the total video viewers comes from mobile devices. This is significant number considering that by November 2016 , YouTube had more than one billion users. YouTube  continues to report that on mobile devices, the average viewing session is more than 40 minutes. The number of mobile users is expected to increase. In an attempt to sustain adaptive streaming in these mobile devices, there is need to come up with a system that adapt to the mobile user connection quality and speed would ensure that the client obtains the highest quality possible
This dissertation with therefore contribute immensely to the knowledge that has been gathered with regards to the improvement of user experience over mobile networks. By focusing on the strategies that would be employed over these networks, it would be possible to identify the ways in which network usage can be improved for video consumption. As a results, a possible benefit includes the realization of optimal solutions for the mobile networks. This would increase efficiency of video viewership for close to 30% of internet users.
HypothesisThe rise in competition in manufacture of high end mobile devices has seen emergence of devices capable of video streaming, video blogging, video broadcasting and video sharing in social networks. However, there has seen some challenges in video communication over broadband networks (including 3G and 4G) due to limitation in bandwidth and the problems of maintaining high reliability and quality . In this context the research hypothesis will therefore be;
Traffic shaping techniques can improve Quality of Experience among the mobile network users.
Limitations of this DissertationThe use of HTTP Adaptive Streaming is becoming widely adopted and this has called for new ways to optimize multimedia streaming toward delivering enhanced Quality of Experience (QoE). One of the key multimedia streaming solutions is HAS, which has presented new challenges and opportunities for content developers, service providers, network operators and device manufacturer . The major limitation of this dissertation is the identification of the specific strategies that have been employed by different networks in the realization of solutions to some of the core problem identified. The realization of data that would be crucial in the compilation of the papers in question would be of negative influence to the companies within those fields. Some of the companies in question have been found to guard this information as trade secrets.
Plan of Dissertation
This dissertation is divided into chapter. Chapter 1 focuses on problem identification while defining the size and scope of the problem. This chapter outlines the objective and explains the importance of improving QoE for the mobile network users. Finally, this chapter outlines the key limitation of this dissertation. Chapter 2 contains the analysis, criticism and summary of the previous studies related to the subject of this thesis. Chapter 3 gives an overview of development of the adaptive streaming up to current protocols.
Chapter 4 and 5 details the techniques of using HAS to improve QoE. This chapter discusses the results of the experiments concurrently. Finally, the dissertation concludes in chapter 6 while giving possible future work.
Theoretical FrameworkDevelopment of video compression technologies in the 1980s coupled with the popularity and growth of the internet in the 1990s largely motivated the idea of streaming video to large number of users. During these times, research in both academia and the computing industry primarily focused on the design and consequent implementation of new protocols of internet video streaming from dedicated servers. These protocols, built on Internet Protocol (IP), were designed to improve Quality of Service (QoS). This witnessed the development of Real-time Transport Protocol (RTP) which defined a standardized packet format for delivering video over IP, RTP Control Protocol (RTCP) which was designed to monitor transmission statistics and QoS and Real Time Streaming Protocol (RTSP) which created and maintained video sessions .
Traditional rate of control schemes did not meet the real time delivery need. It was for this reason rate control and rate shaping protocols were proposed to minimize...
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