"As streaming techniques and wireless access networks become more widely deployed, a streaming multimedia connection with the "last mile" being a wireless network is becoming increasingly common. However, since current streaming techniques are primarily designed for wired networks, streaming multimedia applications can perform poorly in wireless networks. Recent research has shown that the wireless network conditions, such as the wireless link layer rate adaptation, contending traffic, and interference can significantly degrade the performance of streaming media applications. This performance degradation includes increased multimedia frame losses and lower image quality caused by packet loss, and multiple rebuffering events that stop the media playout. This dissertation presents the model, design, implementation and evaluation of an application layer solution for improving streaming multimedia application performance in IEEE 802.11 wireless networks by using enhanced bandwidth estimation techniques. The solution includes two parts: 1) a new Wireless Bandwidth estimation tool (WBest) designed for fast, non-intrusive, accurate estimation of available bandwidth in IEEE 802.11 networks, which can be used by streaming multimedia applications to improve the performance in wireless networks; 2) a Buffer and Rate Optimization for Streaming (BROS) algorithm using WBest to guide the streaming rate selection and initial buffer optimization. WBest and BROS are implemented and incorporated into an emulated streaming client-server system, Emulated Streaming (EmuS), in Linux and evaluated under a variety of wireless conditions. The evaluations show that with WBest and BROS, the performance of streaming multimedia applications in wireless networks can be significantly improved in terms of multimedia frame loss, rebuffer events and buffer delay."

xxii, 413 p. : ill. / Streaming multimedia content over the Internet is extremely popular mainly due to emerging applications such as IPTV, YouTube and e-learning. All these applications require simultaneous streaming of multimedia content from one or multiple sources to a large number of users. Such applications impose unique requirements in terms of server bandwidth and playback delay which are difficult to achieve in a scalable fashion with the traditional client-server architecture. Peer-to-peer (P2P) overlays offer a promising approach to support scalable streaming applications, that we broadly refer to as "P2P streaming". Design of a scalable P2P streaming mechanism that accommodates heterogeneity of peers' bandwidth and copes with dynamics of peer participation while ensuring in-time delivery of the multimedia content to individual peers is extremely challenging. Besides these fundamental challenges, P2P streaming applications are facing practical issues such as encouraging peers' contribution and decreasing the costly inter-ISP P2P traffic. In this dissertation, we study several aspects of live P2P streaming with the goal of improving the performance of such systems. This dissertation can be categorized into two parts as follows. ( i ) We present the design and evaluation of a mesh-based live P2P streaming mechanism, called PRIME. Further, we perform a head-to-head comparison between the two approaches on live P2P streaming, namely tree-based and mesh-based. We demonstrate the superiority of the mesh-based approach. In the quest for a systematic comparison of existing mesh-based solutions on live P2P streaming, we leverage the insights from our design in PRIME and propose an evaluation methodology. Utilizing the evaluation methodology, we compare the performance of existing mesh-based live P2P streaming solutions. ( ii ) From a more practical perspective, we tackle some of the existing practical issues in the deployment of live P2P streaming applications, namely providing incentives for participating peers to contribute their resources and designing ISP-friendly live P2P streaming protocols with the ultimate goal of reducing costly inter-ISP traffic. In the end, this dissertation reveals fundamental trade-offs in the design, comparison and meaningful evaluation of basic and practical live P2P streaming mechanisms under realistic settings. This dissertation includes my previously published and my co-authored materials. / Committee in charge: Prof. Reza Rejaie, Chair; Prof. Virginia Lo; Prof. Jun Li; Prof. David Levin; Prof. Markus Hofmann

Distribution of video contents over the Internet has increased drastically over the past few years. With technological advancements and emergence of social media services, video content sharing has grown exponentially. An increased number of cyber crimes today belong to possession or distribution of illegal video contents over the Internet. Therefore, it is crucial for forensic examiners to have the capability of recovering and analyzing illegal video contents from seized storage devices. File carving is an advanced forensic technique used to recover deleted contents from a storage device even when there is no file system present. After recovering a deleted video file, its contents have to be analyzed manually in order to classify them. This is not only very stressful but also takes a large amount of time. In this thesis we propose a carving approach for streaming multimedia formats that allows forensic examiners to recover individual frames of a video file as images. The contents of these images then can be classified using existing techniques for forensic analysis of image sets. A carving tool based on this approach is developed for MPEG-1 video files. A number of experiments are conducted to evaluate performance of the tool. For each experiment an MPEG-1 file with different encoding parameters is used. Moreover, each experiment contains 18 runs and with each run chunk size of the input MPEG-1 file is varied in order to create different amount of disk fragmentation For video only MPEG-1 files, 87.802 % frames are fully recovered when the chunk size is equal to 124 KB. Where as in the case of MPEG-1 files containing both audio and video data 90.55 % frames are fully recovered when the chunk size is 132 KB.