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Streaming of videos has become the major traffic generator in today's Internet and the video traffic share is still increasing. According to Cisco's annual Visual Networking Index report, in 2012, 60% of the global Internet IP traffic was generated by video streaming services. Furthermore, the study predicts further increase to 73% by 2017. At the same time, advances in the fields of mobile communications and embedded devices lead to a widespread adoption of Internet video enabled mobile and wireless devices (e.g. Smartphones). The report predicts that by 2017, the traffic originating from mobile and wireless devices will exceed the traffic from wired devices and states that mobile video traffic was the source of roughly half of the mobile IP traffic at the end of 2012. With the increasing importance of Internet video streaming in today's world, video content provider find themselves in a highly competitive market where user expectations are high and customer loyalty depends strongly on the user's satisfaction with the provided service. In particular paying customers expect their viewing experience to be the same across all their viewing devices and independently of their currently utilized Internet access technology. However, providing video streaming services is costly in terms of storage space, required bandwidth and generated traffic. Therefore, content providers face a trade-off between the user perceived Quality of Experience (QoE) and the costs for providing the service. Today, a variety of transport and application protocols exist for providing video streaming services, but the one utilized depends on the scenario in mind. Video streaming services can be divided up in three categories: Video conferencing, IPTV and Video-on-Demand services. IPTV and video-conferencing have severe real-time constraints and thus utilize mostly datagram-based protocols like the RTP/UDP protocol for the video transmission. Video-on-Demand services in contrast can profit from pre-encoded content, buffers at the end user's device, and mostly utilize TCP-based protocols in combination with progressive streaming for the media delivery. In recent years, the HTTP protocol on top of the TCP protocol gained widespread popularity as a cost-efficient way to distribute pre-encoded video content to customers via progressive streaming. This is due to the fact that HTTP-based video streaming profits from a well-established infrastructure which was originally implemented to efficiently satisfy the increasing demand for web browsing and file downloads. Large Content Delivery Networks (CDN) are the key components of that distribution infrastructure. CDNs prevent expensive long-haul data traffic and delays by distributing HTTP content to world-wide locations close to the customers. As of 2012, already 53% of the global video traffic in the Internet originates from Content Delivery Networks and that percentage is expected to increase to 65% by the year 2017. Furthermore, HTTP media streaming profits from existing HTTP caching infrastructure, ease of NAT and proxy traversal and firewall friendliness. Video delivery through heterogeneous wired and wireless communications networks is prone to distortions due to insufficient network resources. This is especially true in wireless scenarios, where user mobility and insufficient signal strength can result in a very poor transport service performance (e.g. high packet loss, delays and low and varying bandwidth). A poor performance of the transport in turn may degrade the Quality of Experience as perceived by the user, either due to buffer underruns (i.e. playback interruptions) for TCP-based delivery or image distortions for datagram-based real-time video delivery. In order to overcome QoE degradations due to insufficient network resources, content provider have to consider adaptive video streaming. One possibility to implement this for HTTP/TCP streaming is by partitioning the content into small segments, encode the segments into different quality levels and provide access to the segments and the quality level details (e.g. resolution, average bitrate). During the streaming session, a client-centric adaptation algorithm can use the supplied details to adapt the playback to the current environment. However, a lack of a common HTTP adaptive streaming standard led to multiple proprietary solutions developed by major Internet companies like Microsoft (Smooth Streaming), Apple (HTTP Live Streaming) and Adobe (HTTP Dynamic Streaming) loosely based on the aforementioned principle. In 2012, the ISO/IEC published the Dynamic Adaptive Streaming over HTTP (MPEG-DASH) standard. As of today, DASH is becoming widely accepted with major companies announcing their support or having already implemented the standard into their products. MPEG-DASH is typically used with single layer codecs like H.264/AVC, but recent publications show that scalable video coding can use the existing HTTP infrastructure more efficiently. Furthermore, the layered approach of scalable video coding extends the adaptation options for the client, since already downloaded segments can be enhanced at a later time. The influence of distortions on the perceived QoE for non-adaptive video streaming are well reviewed and published. For HTTP streaming, the QoE of the user is influenced by the initial delay (i.e. the time the client pre-buffers video data) and the length and frequency of playback interruptions due to a depleted video playback buffer. Studies highlight that even low stalling times and frequencies have a negative impact on the QoE of the user and should therefore be avoided. The first contribution of this thesis is the identification of QoE influence factors of adaptive video streaming by the means of crowd-sourcing and a laboratory study. MPEG-DASH does not specify how to adapt the playback to the available bandwidth and therefore the design of a download/adaptation algorithm is left to the developer of the client logic. The second contribution of this thesis is the design of a novel user-centric adaption logic for DASH with SVC. Other download algorithms for segmented HTTP streaming with single layer and scalable video coding have been published lately. However, there is little information about the behavior of these algorithms regarding the identified QoE-influence factors. The third contribution is a user-centric performance evaluation of three existing adaptation algorithms and a comparison to the proposed algorithm. In the performance evaluation we also evaluate the fairness of the algorithms. In one fairness scenario, two clients deploy the same adaptation algorithm and share one Internet connection. For a fair adaptation algorithm, we expect the behavior of the two clients to be identical. In a second fairness scenario, one client shares the Internet connection with a large HTTP file download and we expect an even bandwidth distribution between the video streaming and the file download. The forth contribution of this thesis is an evaluation of the behavior of the algorithms in a two-client and HTTP cross traffic scenario. The remainder of this thesis is structured as follows. Chapter II gives a brief introduction to video coding with H.264, the HTTP adaptive streaming standard MPEG-DASH, the investigated adaptation algorithms and metrics of Quality of Experience (QoE) for video streaming. Chapter III presents the methodology and results of the subjective studies conducted in the course of this thesis to identify the QoE influence factors of adaptive video streaming. In Chapter IV, we introduce the proposed adaptation algorithm and the methodology of the performance evaluation. Chapter V highlights the results of the performance evaluation and compares the investigated adaptation algorithms. Section VI summarizes the main findings and gives an outlook towards QoE-centric management of DASH with SVC.

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The study of phylogeny, i.e. the evolutionary history of species, is a central problem in biology and a key for understanding characteristics of contemporary species. Many problems in this area can be formulated as combinatorial optimisation problems which makes it particularly interesting for computer scientists. The reconstruction of the phylogeny of species can be based on various kinds of data, e.g. morphological properties or characteristics of the genetic information of the species. Maximum parsimony is a popular and widely used method for phylogenetic reconstruction aiming for an explanation of the observed data requiring the least evolutionary changes. A certain property of the genetic information gained much interest for the reconstruction of phylogeny in recent time: the organisation of the genomes of species, i.e. the arrangement of the genes on the chromosomes. But the idea to reconstruct phylogenetic information from gene arrangements has a long history. In Dobzhansky and Sturtevant (1938) it was already pointed out that “a comparison of the different gene arrangements in the same chromosome may, in certain cases, throw light on the historical relationships of these structures, and consequently on the history of the species as a whole”. This kind of data is promising for the study of deep evolutionary relationships because gene arrangements are believed to evolve slowly (Rokas and Holland, 2000). This seems to be the case especially for mitochondrial genomes which are available for a wide range of species (Boore, 1999). The development of methods for the reconstruction of phylogeny from gene arrangement data has made considerable progress during the last years. Prominent examples are the computation of parsimonious evolutionary scenarios, i.e. a shortest sequence of rearrangements transforming one arrangement of genes into another or the length of such a minimal scenario (Hannenhalli and Pevzner, 1995b; Sankoff, 1992; Watterson et al., 1982); the reconstruction of parsimonious phylogenetic trees from gene arrangement data (Bader et al., 2008; Bernt et al., 2007b; Bourque and Pevzner, 2002; Moret et al., 2002a); or the computation of the similarities of gene arrangements (Bergeron et al., 2008a; Heber et al., 2009). 1 1 Introduction The central theme of this work is to provide efficient algorithms for modified versions of fundamental genome rearrangement problems using more plausible rearrangement models. Two types of modified rearrangement models are explored. The first type is to restrict the set of allowed rearrangements as follows. It can be observed that certain groups of genes are preserved during evolution. This may be caused by functional constraints which prevented the destruction (Lathe et al., 2000; Sémon and Duret, 2006; Xie et al., 2003), certain properties of the rearrangements which shaped the gene orders (Eisen et al., 2000; Sankoff, 2002; Tillier and Collins, 2000), or just because no destructive rearrangement happened since the speciation of the gene orders. It can be assumed that gene groups, found in all studied gene orders, are not acquired independently. Accordingly, these gene groups should be preserved in plausible reconstructions of the course of evolution, in particular the gene groups should be present in the reconstructed putative ancestral gene orders. This can be achieved by restricting the set of rearrangements, which are allowed for the reconstruction, to those which preserve the gene groups of the given gene orders. Since it is difficult to determine functionally what a gene group is, it has been proposed to consider common combinatorial structures of the gene orders as gene groups (Marcotte et al., 1999; Overbeek et al., 1999). The second considered modification of the rearrangement model is extending the set of allowed rearrangement types. Different types of rearrangement operations have shuffled the gene orders during evolution. It should be attempted to use the same set of rearrangement operations for the reconstruction otherwise distorted or even wrong phylogenetic conclusions may be obtained in the worst case. Both possibilities have been considered for certain rearrangement problems before. Restricted sets of allowed rearrangements have been used successfully for the computation of parsimonious rearrangement scenarios consisting of inversions only where the gene groups are identified as common intervals (Bérard et al., 2007; Figeac and Varré, 2004). Extending the set of allowed rearrangement operations is a delicate task. On the one hand it is unknown which rearrangements have to be regarded because this is part of the phylogeny to be discovered. On the other hand, efficient exact rearrangement methods including several operations are still rare, in particular when transpositions should be included. For example, the problem to compute shortest rearrangement scenarios including transpositions is still of unknown computational complexity. Currently, only efficient approximation algorithms are known (e.g. Bader and Ohlebusch, 2007; Elias and Hartman, 2006). Two problems have been studied with respect to one or even both of these possibilities in the scope of this work. The first one is the inversion median problem. Given the gene orders of some taxa, this problem asks for potential ancestral gene orders such that the corresponding inversion scenario is parsimonious, i.e. has a minimum length. Solving this problem is an essential component 2 of algorithms for computing phylogenetic trees from gene arrangements (Bourque and Pevzner, 2002; Moret et al., 2002a, 2001). The unconstrained inversion median problem is NP-hard (Caprara, 2003). In Chapter 3 the inversion median problem is studied under the additional constraint to preserve gene groups of the input gene orders. Common intervals, i.e. sets of genes that appear consecutively in the gene orders, are used for modelling gene groups. The problem of finding such ancestral gene orders is called the preserving inversion median problem. Already the problem of finding a shortest inversion scenario for two gene orders is NP-hard (Figeac and Varré, 2004). Mitochondrial gene orders are a rich source for phylogenetic investigations because they are known for more than 1 000 species. Four rearrangement operations are reported at least in the literature to be relevant for the study of mitochondrial gene order evolution (Boore, 1999): That is inversions, transpositions, inverse transpositions, and tandem duplication random loss (TDRL). Efficient methods for a plausible reconstruction of genome rearrangements for mitochondrial gene orders using all four operations are presented in Chapter 4. An important rearrangement operation, in particular for the study of mitochondrial gene orders, is the tandem duplication random loss operation (e.g. Boore, 2000; Mauro et al., 2006). This rearrangement duplicates a part of a gene order followed by the random loss of one of the redundant copies of each gene. The gene order is rearranged depending on which copy is lost. This rearrangement should be regarded for reconstructing phylogeny from gene order data. But the properties of this rearrangement operation have rarely been studied (Bouvel and Rossin, 2009; Chaudhuri et al., 2006). The combinatorial properties of the TDRL operation are studied in Chapter 5. The enumeration and counting of sorting TDRLs, that is TDRL operations reducing the distance, is studied in particular. Closed formulas for computing the number of sorting TDRLs and methods for the enumeration are presented. Furthermore, TDRLs are one of the operations considered in Chapter 4. An interesting property of this rearrangement, distinguishing it from other rearrangements, is its asymmetry. That is the effects of a single TDRL can (in the most cases) not be reversed with a single TDRL. The use of this property for phylogeny reconstruction is studied in Section 4.3. This thesis is structured as follows. The existing approaches obeying similar types of modified rearrangement models as well as important concepts and computational methods to related problems are reviewed in Chapter 2. The combinatorial structures of gene orders that have been proposed for identifying gene groups, in particular common intervals, as well as the computational approaches for their computation are reviewed in Section 2.2. Approaches for computing parsimonious pairwise rearrangement scenarios are outlined in Section 2.3. Methods for the computation genome rearrangement scenarios obeying biologically motivated constraints, as introduced above, are detailed in Section 2.4. The approaches for the inversion median problem are covered in Section 2.5. Methods for the reconstruction of phylogenetic trees from gene arrangement data are briefly outlined in Section 2.6.3 1 Introduction Chapter 3 introduces the new algorithms CIP, ECIP, and TCIP for solving the preserving inversion median problem. The efficiency of the algorithm is empirically studied for simulated as well as mitochondrial data. The description of algorithms CIP and ECIP is based on Bernt et al. (2006b). TCIP has been described in Bernt et al. (2007a, 2008b). But the theoretical foundation of TCIP is extended significantly within this work in order to allow for more than three input permutations. Gene order rearrangement methods that have been developed for the reconstruction of the phylogeny of mitochondrial gene orders are presented in the fourth chapter. The presented algorithm CREx computes rearrangement scenarios for pairs of gene orders. CREx regards the four types of rearrangement operations which are important for mitochondrial gene orders. Based on CREx the algorithm TreeREx for assigning rearrangement events to a given tree is developed. The quality of the CREx reconstructions is analysed in a large empirical study for simulated gene orders. The results of TreeREx are analysed for several mitochondrial data sets. Algorithms CREx and TreeREx have been published in Bernt et al. (2008a, 2007c). The analysis of the mitochondrial gene orders of Echinodermata was included in Perseke et al. (2008). Additionally, a new and simple method is presented to explore the potential of the CREx method. The new method is applied to the complete mitochondrial data set. The problem of enumerating and counting sorting TDRLs is studied in Chapter 5. The theoretical results are covered to a large extent by Bernt et al. (2009b). The missing combinatorial explanation for some of the presented formulas is given here for the first time. Therefor, a new method for the enumeration and counting of sorting TDRLs has been developed (Bernt et al., 2009a).