An Efficient Transport Protocol for delivery of Multimedia An Efficient Transport Protocol for delivery of Multimedia Content in Wireless Grids

A grid computing system is designed for solving complicated scientific and commercial problems effectively,whereas mobile computing is a traditional distributed system having computing capability with mobility and adopting wireless communications. Media and Entertainment fields can take advantage from both paradigms by applying its usage in gaming applications and multimedia data management. Multimedia data has to be stored and retrieved in an efficient and effective manner to put it in use. In this paper, we proposed an application layer protocol for delivery of multimedia data in wireless girds i.e. multimedia grid protocol (MMGP). To make streaming efficient a new video compression algorithm called dWave is designed and embedded in the proposed protocol. This protocol will provide faster, reliable access and render an imperceptible QoS in delivering multimedia in wireless grid environment and tackles the challenging issues such as i) intermittent connectivity, ii) device heterogeneity, iii) weak security and iv) device mobility.Comment: 20 pages, 15 figures, Peer Reviewed Journa

Real-time video streaming using peer-to-peer for video distribution

The growth of the Internet has led to research and development of several new and useful applications including video streaming. Commercial experiments are underway to determine the feasibility of multimedia broadcasting using packet based data networks alongside traditional over-the-air broadcasting. Broadcasting companies are offering low cost or free versions of video content online to both guage and at the same time generate popularity. In addition to television broadcasting, video streaming is used in a number of application areas including video conferencing, telecommuting and long distance education. Large scale video streaming has not become as widespread or widely deployed as could be expected. The reason for this is the high bandwidth requirement (and thus high cost) associated with video data. Provision of a constant stream of video data on a medium to large scale typically consumes a significant amount of bandwidth. An effect of this is that encoding bit rates are lowered and consequently video quality is degraded resulting in even slower uptake rates for video streaming services. The aim of this dissertation is to investigate peer-to-peer streaming as a potential solution to this bandwidth problem. The proposed peer-to-peer based solution relies on end user co-operation for video data distribution. This approach is highly effective in reducing the outgoing bandwidth requirement for the video streaming server. End users redistribute received video chunks amongst their respective peers and in so doing increase the potential capacity of the entire network for supporting more clients. A secondary effect of such a system is that encoding capabilities (including higher encoding bit rates or encoding of additional sub-channels) can be enhanced. Peer-to-peer distribution enables any regular user to stream video to large streaming networks with many viewers. This research includes a detailed overview of the fields of video streaming and peer-to-peer networking. Techniques for optimal video preparation and data distribution were investigated. A variety of academic and commercial peer-to-peer based multimedia broadcasting systems were analysed as a means to further define and place the proposed implementation in context with respect to other peercasting implementations. A proof-of-concept of the proposed implementation was developed, mathematically analyzed and simulated in a typical deployment scenario. Analysis was carried out to predict simulation performance and as a form of design evaluation and verification. The analysis highlighted some critical areas which resulted in adaptations to the initial design as well as conditions under which performance can be guaranteed. A simulation of the proof-of-concept system was used to determine the extent of bandwidth savings for the video server. The aim of the simulations was to show that it is possible to encode and deliver video data in real time over a peer-to-peer network. The proposed system achieved expectations and showed significant bandwidth savings for a sustantially large video streaming audience. The implementation was able to encode video in real time and continually stream video packets on time to connected peers while continually supporting network growth by connecting additional peers (or stream viewers). The system performed well and showed good performance under typical real world restrictions on available bandwith capacity.Dissertation (MEng)--University of Pretoria, 2009.Electrical, Electronic and Computer Engineeringunrestricte

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

Adaptation and Robustness in Peer-to-Peer Streaming

The rapid development of network communication infrastructure enables networked multimedia streaming applications ranging from on-demand video streaming to highly interactive video conferencing. Peer-to-Peer (P2P) technologies have emerged as a powerful and popular paradigm for bringing such emerging multimedia services to a large number of users. The essential advantage of P2P systems is that the system capacity scales up when more peers join, as peer upload capacity is utilized. However, providing satisfactory streaming services over P2P networks is challenging because of their inherent instability and unreliability and the limited adaptability of traditional video coding techniques. On one hand, different from dedicated servers, users may not have enough bandwidth to serve other users as most user connections are asymmetric in their upload and download capacity, and they are heterogeneous in terms of bandwidth and preferences. In addition, users can join and leave the system at any time as there are no guarantees on their contribution to the system. On the other hand, although traditional video coding techniques are efficient in terms of resource consumption, compression ratio, and coding and decoding speed, they do not support scalable modes efficiently as such modes come along with high computation cost. Consequently, in traditional P2P streaming systems, the bit rate (the video quality) of media streams is determined based on the capacities of the low-end users, i.e. the lowest common denominator, to make sure that most of their users can perceive acceptable quality. This causes two critical limitations of the current P2P streaming systems. First, users perceive the same quality regardless of their bandwidth capacity, i.e., no differentiated QoS. Second, with the current best-effort Internet and peer dynamics, the streaming quality at each peer is easily impaired, i.e., no continuous playback. Recently, multiple layer codec research has become more refined, as SVC (the scalable extension of the H.264/AVC standard) has been standardized with a bit rate overhead of around 10% and an indistinguishable visual quality compared to the state of the art single layer codec. The hypothesis of this research work is that the adaptable coding technique can bring significant benefits to P2P streaming as it enables adaptability in P2P streaming. In addition, to improve the robustness of the system to network fluctuations and peer dynamics, network coding and social networking are also applied. The overall goal of this research is to achieve adaptive and robust P2P streaming services, which are believed to be the next generation of P2P streaming on the Internet. Several major contributions are presented in this dissertation. First, to use SVC in P2P streaming, a segmentation method to segment SVC streams into scalable units is proposed such that they can be delivered adaptively by the P2P paradigm. The method is demonstrated to be able to preserve the scalability features of a stream, i.e., adaptation can be applied on segments and the re-generated stream at each peer is a valid stream. Second, a novel and complete adaptive P2P streaming protocol, named Chameleon, is presented. Chameleon uses the segmentation method to use SVC and combine it with network coding in P2P streaming to achieve high performance streaming. The core of Chameleon is studied, including neighbor selection, quality adaptation, receiver-driven peer coordination, and sender selection, with different design options. Experiments on Chameleon reveal that overlay construction is important to system performance, and traditional gossip-based protocols are not good enough for layered P2P streaming. Therefore, third, a SCAMP-based neighbor selection protocol and a peer sampling-based membership management protocol for layered P2P streaming are proposed. These gossip-based protocols are quality- and context-aware as they form robust and adaptable overlays for layered P2P streaming so that high capacity peers have a higher priority to be located at good positions in the overlay, e.g. closer to the server, and peers with similar capacity are connected to each other to better utilize resources. Fourth, to better deal with peer dynamics, Stir, a social-based P2P streaming system, is suggested. In Stir, the novel idea of spontaneous social networking is introduced. Stir users who join the same streaming session can make friends and communicate with each other by cheap yet efficient communication means, e.g., instant messaging and Twitter-like commenting. Such friendship networks are exploited directly by the underlying social-based P2P streaming protocol. The tight integration between the high level social networking of users and the low level overlay of peers is demonstrated to be beneficial in dealing with high churn rates and providing personalized streaming services. Finally, as the approaches are about different aspects of adaptive and robust P2P streaming, to complete the picture, Chameleon++, which combines Chameleon and Stir, is presented. The design and the evaluation of Chameleon++ demonstrate the feasibility and the benefits of the approaches, and the consistency of the study