Dataset on usage of a live & VoD P2P IPTV service

This paper presents a dataset of user statistics collected from a P2P multimedia service infrastructure that delivers both live and on-demand content in high quality to users via different platforms: PC/Mac, and set top boxes. The dataset covers a period of seven months starting from October 2011, exposing a total of over 94k system statistic reports from thousands of user devices at a fine granularity. Such rich data source is made available to fellow researchers to aid in developing better understanding of video delivery mechanisms, user behaviour, and programme popularity evolution

A novel adaptive schema to facilitates playback switching technique for video delivery in dense LTE cellular heterogeneous network environments

The services of the Video on Demand (VoD) are currently based on the developments of the technology of the digital video and the network’s high speed. The files of the video are retrieved from many viewers according to the permission, which is given by VoD services. The remote VoD servers conduct this access. A server permits the user to choose videos anywhere/anytime in order to enjoy a unified control of the video playback. In this paper, a novel adaptive method is produced in order to deliver various facilities of the VoD to all mobile nodes that are moving within several networks. This process is performed via mobility modules within the produced method since it applies a seamless playback technique for retrieving the facilities of the VoD through environments of heterogeneous networks. The main components comprise two servers, which are named as the GMF and the LMF. The performance of the simulation is tested for checking clients’ movements through different networks with different sizes and speeds, which are buffered in the storage. It is found to be proven from the results that the handoff latency has various types of rapidity. The method applies smooth connections and delivers various facilities of the VoD. Meantime, the mobile device transfers through different networks. This implies that the system transports video segments easily without encountering any notable effects.In the experimental analysis for the Slow movements mobile node handoff latency (8 Km/hour or 4 m/s) ,the mobile device’s speed reaches 4m/s, the delay time ranges from 1 to 1.2 seconds in the proposed system, while the MobiVoD system ranges from 1.1 to 1.5. In the proposed technique reaches 1.1026 seconds forming the required time of a mobile device that is switching from a single network to its adjacent one. while the handoff termination average in the MobiVoD reaches 1.3098 seconds. Medium movement mobile node handoff latency (21 Km/ hour or 8 m/s) The average handoff time for the proposed system reaches 1.1057 seconds where this implies that this technique can seamlessly provide several segments of a video segments regardless of any encountered problems. while the average handoff time for the MobiVoD reaches 1.53006623 seconds. Furthermore, Fast movement mobile node handoff latency (390 Km/ hour or 20 m/s). The average time latency of the proposed technique reaches 1.0964 seconds, while the MobiVoD System reaches to 1.668225 seconds

Characterizing Popularity Dynamics of User-generated Videos: A Category-based Study of YouTube

Understanding the growth pattern of content popularity has become a subject of immense interest to Internet service providers, content makers and on-line advertisers. This understanding is also important for the sustainable development of content distribution systems. As an approach to comprehend the characteristics of this growth pattern, a significant amount of research has been done in analyzing the popularity growth patterns of YouTube videos. Unfortunately, no work has been done that intensively investigates the popularity patterns of YouTube videos based on video object category. In this thesis, an in-depth analysis of the popularity pattern of YouTube videos is performed, considering the categories of videos. Metadata and request patterns were collected by employing category-specific YouTube crawlers. The request patterns were observed for a period of five months. Results confirm that the time varying popularity of di fferent YouTube categories are conspicuously diff erent, in spite of having sets of categories with very similar viewing patterns. In particular, News and Sports exhibit similar growth curves, as do Music and Film. While for some categories views at early ages can be used to predict future popularity, for some others predicting future popularity is a challenging task and require more sophisticated techniques, e.g., time-series clustering. The outcomes of these analyses are instrumental towards designing a reliable workload generator, which can be further used to evaluate diff erent caching policies for YouTube and similar sites. In this thesis, workload generators for four of the YouTube categories are developed. Performance of these workload generators suggest that a complete category-specific workload generator can be developed using time-series clustering. Patterns of users' interaction with YouTube videos are also analyzed from a dataset collected in a local network. This shows the possible ways of improving the performance of Peer-to-Peer video distribution technique along with a new video recommendation method

Enabling Large-Scale Peer-to-Peer Stored Video Streaming Service with QoS Support

This research aims to enable a large-scale, high-volume, peer-to-peer, stored-video streaming service over the Internet, such as on-line DVD rentals. P2P allows a group of dynamically organized users to cooperatively support content discovery and distribution services without needing to employ a central server. P2P has the potential to overcome the scalability issue associated with client-server based video distribution networks; however, it brings a new set of challenges. This research addresses the following five technical challenges associated with the distribution of streaming video over the P2P network: 1) allow users with limited transmit bandwidth capacity to become contributing sources, 2) support the advertisement and discovery of time-changing and time-bounded video frame availability, 3) Minimize the impact of distribution source losses during video playback, 4) incorporate user mobility information in the selection of distribution sources, and 5) design a streaming network architecture that enables above functionalities.To meet the above requirements, we propose a video distribution network model based on a hybrid architecture between client-server and P2P. In this model, a video is divided into a sequence of small segments and each user executes a scheduling algorithm to determine the order, the timing, and the rate of segment retrievals from other users. The model also employs an advertisement and discovery scheme which incorporates parameters of the scheduling algorithm to allow users to share their life-time of video segment availability information in one advertisement and one query. An accompanying QoS scheme allows reduction in the number of video playback interruptions while one or more distribution sources depart from the service prematurely.The simulation study shows that the proposed model and associated schemes greatly alleviate the bandwidth requirement of the video distribution server, especially when the number of participating users grows large. As much as 90% of load reduction was observed in some experiments when compared to a traditional client-server based video distribution service. A significant reduction is also observed in the number of video presentation interruptions when the proposed QoS scheme is incorporated in the distribution process while certain percentages of distribution sources depart from the service unexpectedly

A Buffering strategy for stabilizing network data rates

In the past ten years, there has been substantial growth in the area of networked applications. The expansion of such Internet applications as USENET and the world wide web has lead to greater demand for faster and higher quality data transmissions. However, the Internet tends to have a bursty traffic pattern. Such a pattern interferes with an application\u27s ability to receive a data stream at a constant rate. A constant rate is necessary for real-time, networked multimedia applications to be able to provide a high quality of service to the user. This thesis proposes a buffering strategy that stabilizes a bursty data stream. It uses buffered data to present a client application with a constant data stream. An implementation of the strategy was produced using the Java programming language. Results indicate that networked multimedia applications that use this strategy can provide a higher quality of service than applications that do not

Popularity Characterization and Modelling for User-generated Videos

User-generated content systems such as YouTube have become highly popular. It is difficult to under- stand and predict content popularity in such systems. Characterizing and modelling content popularity can provide deeper insights into system design trade-offs and enable prediction of system behaviour in advance. Borghol et al. collected two datasets of YouTube video weekly view counts over eight months in 2008/09, namely a “recently-uploaded” dataset and a “keyword-search” dataset, and analyzed the popular- ity characteristics of the videos in the recently-uploaded dataset including the video popularity evolution over time. Based on the observed characteristics, they developed a model that can generate synthetic video weekly view counts whose characteristics with respect to video popularity evolution match those observed in the recently-uploaded dataset. For this thesis, new weekly view count data was collected over two months in 2011 for the videos in the recently-uploaded and keyword-search datasets of Borghol et al. This data was used to evaluate the accuracy of the Borghol et al. model when used to generate synthetic view counts for a much longer time period than the eight month period previously considered. Although the model yielded distributions of total (lifetime) video view counts that match the empirical distributions, significant differences between the model and em- pirical data were observed. These differences appear to arise because of particular popularity characteristics that change over time rather than being week-invariant as assumed in the model. This thesis also characterizes how video popularity evolves beyond the eight month period considered by Borghol et al., and studies the characteristics of the keyword-search dataset with respect to content popu- larity, popularity evolution, and sampling biases. Finally, the thesis studies the popularity characteristics of the videos in the recently-uploaded and keyword-search datasets for which additional view count data could not be collected, owing to the removal of these videos from YouTube