Figure 1 - uploaded by Tiziana Catarci
Content may be subject to copyright.
Source publication
Multimedia database systems require not only that correct results are delivered with acceptable delay, but also that they're delivered in real time with acceptable quality. A constraint-based rule language can serve as the foundation for providing this level of quality of service in video databases.
Context in source publication
Context 1
... abstract architecture, inspired from the one given in [10], is designed to provide a man- agement system for video data. Figure 1 shows the block level architecture of a video data management system integrating quality of service management. The function of each of these blocks is described below. ...
Similar publications
In this paper we propose efficient buffer prefetching and replacement policies for continuous-media servers that support content-based search and retrieval. The new policies are based on the knowledge collected from the content-based search manager and the streaming manager. We show that by integrating the knowledge from the search and streaming co...
Citations
... Thus, packets transmitted even at almost same time (consecutive packets) experience large variations in end to end delay. Jitter is a considerable issue in video streaming and can degrade QoS [81], [82]. Jitter requirements vary as per application ranging from 10ms-50ms e.g., VoD streaming (≤ 50ms) [83], videoconferencing and interactive video streaming (≤ 30ms) [84]. ...
5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-to-end communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultra-reliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-of-the-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.
... Thus, packets transmitted even at almost same time (consecutive packets) experience large variations in end to end delay. Jitter is a considerable issue in video streaming and can degrade QoS [81], [82]. Jitter requirements vary as per application ranging from 10ms˘50ms e.g., VoD streaming (≤ 50ms) [83], videoconferencing and interactive video streaming (≤ 30ms) [84]. ...
5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-to-end communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultra-reliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-of-the-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.
... In that paper, we extend the query processing/ optimization module of a multimedia DBMS to handle quality in queries as a core DBMS functionality. Two other related works in multimedia databases discuss quality specification[11]and quality model[12]. None of the above deals with replication of copies with different qualities. ...
In contrast to other database applications, multimedia data can have a wide range of quality parameters, such as spatial and temporal resolution and compression format. Users can request data with specific quality requirements due to the needs of their application or the limitations of their resources. The database can support multiple qualities by converting data from the original (high) quality to another (lower) quality to support a user's query or precompute and store multiple quality replicas of data items. On-the-fly conversion of multimedia data (such as video transcoding) is very CPU intensive and can limit the level of concurrent access supported by the database. Storing all possible replicas, on the other hand, requires unacceptable increases in storage requirements. In this paper, we address the problem of multiple-quality replica selection subject to an overall storage constraint. We establish that the problem is NP-hard and provide heuristic solutions under two different system models: hard-quality and soft-quality. Under the soft-quality model, users are willing to negotiate their quality needs, as opposed to the hard-quality system wherein users can only accept the exact quality requested. Extensive simulations show that our algorithm performs significantly better than other heuristics. Our algorithms are flexible in that they can be extended to deal with changes in query pattern
The worldwide growth in broadband access and multimedia traffic has led to an increas- ing need for Quality-of-Service (QoS) in networks. Multimedia traffic and real time applications require stable, reliable, and predictable networks that guarantee packet de- livery under QoS constraints. This has led to the growth of Self-Aware Networks, that use QoS driven approaches to provide more stable and more reliable communications than the current “best-effort” Internet architecture. Such a network is the Cognitive Packet Network (CPN), which addresses the QoS requirements of the users by routing their traffic according to their requests.
In all types of networks, with CPN not constituting an exception, congestion and reli- ability need to be carefully examined. High demand and network congestion can prevent multimedia applications and users from obtaining the network service that they require for a successful operation. Additionally, node failures caused by network threats, such as computer worms, impair the service of legitimate users by incapacitating the network temporarily or permanently. Our aim is to satisfy all users’ QoS requirements through- out the lifetime of their connections by controlling traffic congestion and protecting the network with adaptive methodologies that allow it to gracefully survive attacks.
Admission Control (AC) is a way to control traffic congestion and best satisfy the users’ QoS requirements without overprovisioning. This thesis presents a measurement- based AC mechanism, which improves the performance of a Self-Aware Network and provides QoS throughout the lifetime of all connections. We propose a multiple criteria AC algorithm, where the users can specify desired QoS metrics and their values. Our scheme decides whether a new connection should be accepted in the network by esti- mating both the necessary resources and its impact on the existing connections. The estimation is based on measurements of the QoS metrics on each link of the network before and after the transmission of probe packets. We have developed an algorithm based on a novel algebra of QoS metrics, inspired by Warshall’s algorithm, which iden- tifies whether there is a feasible path to accommodate the new flow without affecting the existing users.
We also present an experimental performance analysis of the CPN protocol in the presence of emulated network worms, and we propose a failure detection element that further improves its resilience to failures. Our proposed enhancement exploits the fact that each CPN packet keeps route and timestamp information in its header. When a packet reaches its destination, it sends back this information with an acknowledgement packet which follows the reverse route. In our scheme, each node detects whether it is part of a failed path, regardless of how far the failure occurs, and takes action only for the flows that are affected by the failure and are routed through that node.
The role of multimedia application in our life has dramatically increased in the last few years. Multimedia streaming is more significant than downloading ways in multimedia applications from bandwidth and delay reasons. Multimedia contents are in nature distributed in nodes of grid. Streaming media are still not well resolved in grid environment. This thesis proposes an agent-based framework for grid called GridMSS. The main characteristics of that are highlighted by its promising QoS supporting such as accessibility, low latency and reliability, its flexibility and packet allocation mechanisms of nodes. Here an agent searches for requested contents by communicating with other agents and in order to retrieve contents related to users' query without delay, an allocation algorithm is performed according to available bandwidth of each node. GridMSS provides a high performance contents streaming framework for a large population of users and ensures the QoS in terms of accessibility, low latency and reliability. The empirical results showed that the framework was efficient enough to be used in grid environments.
Current academic Internet environment has enabled fast transfers of huge amounts of data, and has made high quality multimedia
and collaborative applications a reality. This article describes a model for distributed multimedia retrieval which performs
the retrieval of different multimedia to a variety of clients using learning automata algorithm named LAGridMSS. LAGridMSS
allocates a proportion of bandwidth of each node for sending a specified file, and then applies learning automata to allocate
packets of files to each node that contains the context. The files’ popularity and the remainder bandwidth of nodes are two
main factors here for allocating packets to each node.
Simulation results show improvements in proposed model, LAGridMSS, in some QoS factors such as delay, jitter, and reliability
compared to previous multimedia retrieval system such as GridMSS and GridMedia.
As the Internet access over broadband networks is growing, multimedia communications with video using MPEG will be widely used. User level QoS will be needed at ubiquitous and multimedia computing environment using MPEG, affects directly on human's sense. We conducted an experiment in order to investigate relation between MPEG encoding bit rates and user level QoS. The result shows that there is a threshold of user level QoS between 200 and 400 Kbps of our video streaming contents.
