Semantic based QoS provisioning for Wireline and Wireless Networks






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Semantic –based QoS provisioning for Wireline and Wireless Networks


National Technical University of Athens

Computer Vision & Photogrammetry Lab.

157 73, Athens, GREECE, email {}

Abstract: - This paper aims at significantly extending existing QoS architectures for wireline and wireless networks by incorporating content semantics in the architectural model. In existing QoS architectures (e.g. DiffServ), the transmitted entities (content) have their own priorities over other entities, but no effort is made to analyze the content in its semantic components (content objects), even though they have different characteristics and requirements, and should therefore receive different QoS treatment. In our approach, we take into consideration the properties of the transmitted content, decompose it into different content objects and provide QoS differentiation based on content semantics, while taking into account users’ preferences and resource capabilities. It is clear that a semantic–based QoS architecture significantly outperforms, in user-perceived quality, static (monolithic) architectures, where all the transmitted content of a given application is handled in a unified way. In particular, we develop a semantic-aware multimedia delivery chain, which includes (a) voice and video multimedia sources, (b) context descriptors generation, (c) context-to-network-semantic translators and (d) context-to-network-semantic capable network devices.

Key-Words: - Visual Semantics, QoS provisioning, MPEG-7/21

1 Introduction

Despite recent increases in network capacities, current network infrastructures are not able to provide to the users satisfactory multimedia content delivery. This is because multimedia content imposes great bandwidth requirements, even in compressed domain, challenging contemporary networks. The situation becomes worse when many users wish to transmit multimedia content.

Current networking methods do not exploit the

semantics of the transmitted data. They use advanced scheduling and resource allocation methods for the delivery of text and/or multimedia content in a QoS-differentiated manner, but they assume the network handles all data of a certain type or application in the same way, regardless of the actual content being transmitted. The purpose of this paper is to significantly extend existing QoS architectures by incorporating semantics in the architectural model, resulting in a Semantic QoS framework.

Our concept may be clarified by applying it to an everyday activity, such as watching a football match. The various instances of the match have a content (i.e. goals, passing game, half time break etc), and the content of each instance has its own importance (semantics). From a network

perspective, it is the semantics of the content that determine the QoS the content should receive from the network. Returning to the football match example, a goal is highly important and should be given priority in the network, whereas half-time break is not at all important and may be delayed or even discarded by the network.

There are two traditional ways to provide different QoS levels for packets within the network: IntServ (Integrated Services) and Diffserv (Differentiated Services). Intserv focuses on per flow-QoS and it does not seem to naturally support semantic QoS, where packets within the same flow may have to be treated differently based on their content. Diffserv is able to provide more fine-grain QoS differentiation by labelling packets with a priority stamp and using this label for QoS management, which makes it a more natural choice for implementing Semantic QoS within the network. Diffserv is one of the two approaches we use in this paper for implementing the semantic QoS idea. The second, and more challenging approach is based on semantic-capable open source IP routers, to be referred to as Open Semantic Routers. Open Semantic Routing replaces the static QoS provisioning mechanisms (prioritization and forwarding) that DiffServ offers, with fully dynamic


ones. Open Semantic Routers interpret the semantics of the packets in the network, using a local programmable database, and prioritize/forward the packets accordingly.

1.1Innovation Issues

Our research enables the deployment of ubiquitous network infrastructures and architectures. In particular,

(A) Convergence and interoperability of heterogeneous mobile and broadband network technologies: Our approach aims at developing a global QoS provisioning mechanism that is based on semantics, rather than applications or underlying networks.

(B) Context awareness: The multimedia delivery chain is fully semantic-aware, taking into consideration not only the context of the delivered data, but also the context importance (semantics), as well.

(C) Optimised traffic processing between core and edge network: The proposed architecture provides optimized traffic processing between the core and edge network by (a) implementing a programmable semantic-aware edge-to-core interface and (b) enforcing semantics that are common to both the edge (multimedia sources) and core (delivery) network.

2 Previous Works

In our approach, we extend current state-of-the art in QoS provisioning by looking inside the data, that is, at the properties of the data. The semantic-based delivery network architecture is preferable over the DiffServ architecture since there are no predefined traffic classes; instead, packets are classified and forwarded independently according to their semantics.

2.1 Research work on QoS provisioning

Quality-of-Service (QoS) provisioning for multimedia delivery is a challenging task, due to the strict requirements that multimedia impose on the end-to-end delivery requirements in terms of bandwidth, delay, delay jitter and packet loss ratio [1]-[3]. This comes in full contrast with the Internet, which inherently offers only best-effort delivery services to its users. The gap between multimedia requirements and the best-effort delivery of the Internet has been bridged by the differentiated-services (DiffServ) architecture that specifies a simple, scalable and coarse-grained mechanism for

classifying and forwarding IP packets [4]-[6].

DiffServ operates on the principle of traffic classification, where each data packet is assigned to a traffic class (per-hop-behaviors). Traffic classes, as well as their priority levels, have been predefined by the DiffServ network operator and are managed so as to ensure preferential treatment for higher-priority traffic on the network. The DiffServ architecture, however, allows only for static traffic class definition, prioritization and management, resulting in non-optimal bandwidth utilization. Specifically, DiffServ traffic classes are statically assigned priority and at least one service class (the Expedited Forwarding traffic class) is allocated higher priority and a fixed portion of the available bandwidth. This, in turn, means that low priority packets are not forwarded even if available bandwidth exists (when there are no high priority packets). Each stream that belongs to the Expedited Forwarding traffic class uses up a portion of the bandwidth whether the multimedia source is active or not. As such, it is not possible to provide full statistical multiplexing within the Expedited Forwarding traffic class and new multimedia delivery requests may not be accepted.


Research Work on Multimedia

In general, metadata include thematic, semantic and syntactic descriptors of the data they refer to. Depending on the different stages of processing that multimedia content undergoes, metadata similarly pass through the different production stages since metadata may be produced, modified, and consumed by all actors involved in the content production-consumption chain [7]. For instance at the production phase, we need to include Metadata for Production, while at the consumption phase metadata for Consumption.

The first type of metadata is generated during or after content production. During production, content producers generate globally valid metadata such as creation information (about authors, actors, date of production, and so on). Additional in a postproduction stage typically low-level features are extracted, such as color histograms or shape recognition or through human intervention high-level semantic information, such as scene descriptions or emotional impressions). These data are responsible for detecting appropriate adaptation schemes for the content so as to meet Quality of Service (QoS) requirements and provide personalized experience to the users. Even proxies


and routers might take advantage of metadata to carry out efficient adaptation. As a result, metadata can be seen as a differentiated framework in which resources can be classified in many different ways simultaneously, to the point that a static tree structure becomes obsolete.

The recent advances in ubiquitous and mobile computing have stimulated a universal multimedia access (UMA) model as an emerging component for the next generation multimedia applications. The basic concept underlying UMA is universal or seamless access to multimedia content, by automatic selection and adaptation of content based on the user’s environment [8]. This boosts a series of new applications in the area of media search and retrieval, summarization and multimedia content annotation. MPEG-7, formally named "Multimedia Content Description Interface", is a standard for describing the audiovisual content data that supports some degree of interpretation of the information meaning [9]. MPEG-7 provides description schemes to describe content in XML to facilitate search, index, and filtering of audio-visual data.


Research work on Open Source

Routing Software

Open source routing software has been extensively investigated as a means of implementing router research prototypes on personal computers [10]-[11], since network equipment vendors are not willing to provide access to the software operating their hardware. As such, researchers are able to develop novel routing mechanisms either by (a) using an open source routing platforms (XORP or Quagga, which both implement the TCP/IP stack and network protocols such as RIP, OSPF, BGP, IGMP and SNMP), or (b) modifying the TCP/IP stack of Linux systems. Reported work on open source routers mainly focuses on protocol optimization and extension issues], still very little effort has been allocated in the area of equipping software routers with QoS mechanisms.

3. Network Semantics Data Tagging

The purpose of this module is to tag the multimedia information in a way that takes into account the semantics of the content and is understandable and easily interpretable by the network. Multimedia tagging is performed at the application layer. This information is then delivered to the network layer to be used for efficient and semantic-based Quality of Service. To achieve

module goals, the following components are adopted.

3.1 Networked Voice Tagging

Voice constitutes one of the main multimedia components of future networks. Voice applications are anticipated to gain relevance in the forthcoming years due to the expected widespread adoption of Voice over IP technologies. However, during a voice transmission, not all voice data is of the same importance and it should be handled in the same way. For example, the silent parts should be transmitted with less importance than the human speech parts. In order to perform this discrimination, a voice characterization scheme is developed. Although automatic voice and speech processing has attracted great research attention in the last years, in our approach only the semantic voice descriptors that can be exploited by the network should be automatically detected.

Application of intelligent speech to text algorithms, for example, is not useful since this metadata is very difficult to interpret by the network layer. For this reason emphasis is put on networked voice semantics such as the ones of (i) classifying speech signals, e. g., whispers, announcement etc., (ii) detecting human voice in sound signals, (iii) understanding emotional factors in speech signals, (iv) classifying audio content like explosions, firings, car racing, airplanes etc.

3.2 Networked Video Tagging

Although several video processing algorithms have been presented in the literature video analysis is constrained with respect to network functionalities. Therefore, video analysis is oriented to extracting those descriptors that are useful for semantic QoS communication.

Video System Descriptors: These descriptors refer to the system video information that is useful for the network. Examples of these descriptors are the type of video frames, the type of coding used, the interrelation among the frames, and so on. For instance, according to the MPEG-2/4 standard, three different types of coding are supported, the Intra (I), the Predicted (P) and the Bi-directionally (B) predicted frame coding. Only the I frames are independent. The P and B frames, instead, are coded with respect to the I and/or the B frames. Losing I frames has more impact on video quality than losing P frames, since, without the correlated I frames, none of the P frames can be decoded.


refer to the visual information of the video frames. Examples include the (a) motion activity of a video shot, (b) content information that a frame or a sequence of frames has, (c) number of main objects a video segment comprises, and (d) the appearance of humans in a video segment. Each of the aforementioned visual descriptors requires specialized video processing algorithms

The networked video system and visual descriptors should be estimated both automatically and in real time. Usually video information is encoded based on MPEG-2/4 or similar standards. For this reason, the aforementioned video processing schemes should ideally be performed in the compressed domain so that the video stream needs not be decoded.

3.3: Content Rights Description

This section describes the rights of the content. For this purpose, an interoperable schema is presented. The schema is based on an extension of the XML language, the Right Expression Language (REL). REL is composed into four different elements, which are described with (a)

Principal-identifies an entity such as the person, organisation, or device to which rights are granted. , (b)

Right-specifies the activity or action that a principal can be granted to exercise against some resource, (c)Resource- identifies an object which the principal can be granted a right, (d) it can be a digital work, a service or a piece of information that can be owned by a principal and (e)

condition-specifies one or more conditions that must be met before the right can be exercised.

4. Semantic-based Diffserv


DiffServ is a network-oriented mechanism, which does not take into consideration the semantics of the transported traffic, and as such is not compatible with context-oriented QoS provision. Our research for implementing Semantic-to-DiffServ translation is: (a) to define DiffServ service classes that correspond to QoS requirements for multimedia purposes, including required bandwidth, maximum delay jitter and maximum packet loss ratio, (b) to develop an interface application for assigning the incoming traffic to DiffServ classes based upon the semantic descriptors, and (c) to develop the necessary DiffServ components (policies) that enforces the QoS requirements in the underlying DiffServ network.

Figure 1: Semantic-to-DiffServ translation.

4.1: Semantic to DiffServ Translation

We define DiffServ service classes that are required to provide QoS in multimedia applications. The classes are as general (as few) as possible, to allow for simple management and operation of the DiffServ network, still they are substantially differentiated to one another so that they describe the best way possible the multiple-level QoS requirements of multimedia applications. Classes are differentiated with respect to the required bandwidth, delay jitter and packet loss. Our approach is fully compatible with the DiffServ recommendations. Thus, classes are encoded on the Differentiated Services Code Point (DSCP) of the respective IP packets. The following classes are defined (see Figure 1):

(A) An Expedited Forwarding (EF) service class for low delay, low loss and low jitter. The EF service class is required for real-time voice, video and other real-time multimedia services. DSCP value is 101110 according to the DiffServ recommendations.

(B) Several Assured Forwarding (AF) service classes for assurance of multimedia delivery. Multimedia traffic is assigned to the AF service classes with respect to the packet losses it can sustain. DSCP values is be assigned according to RFC 2597.


DSCP value is 000000 according to the DiffServ recommendations.

4.2 Network Aspects

This module implements service class specific policies that enable the QoS enforcement on the DiffServ network. The policies are implemented using the tools that are available by the network equipment vendor(s). The main network aspects during our implementation of the service class policies are:

Reserved bandwidth. The EF service class are assigned a fixed percentage of the network bandwidth, while the AF and default service classes

are assigned a guaranteed minimum bandwidth

percentage. The exact values of bandwidth percentages are determined by the bandwidth requirements of the multimedia applications that are included in each service class.

Prioritization. Several prioritization schemes between classes are considered to assign priority and achieve fairness. Prioritization schemes include

strict priority queuing, fair queuing and weighted fair queuing.

5. Semantic-based QoS Routing


The proposed algorithm to form fields is highly distributed and based on local decisions, which makes it scalable and efficient to react to changes in the network topology. The introduced attribute field layer allows to structure and aggregate routing and service information and thus reduces the signaling overhead. Data packets are tagged with a description of the content and forwarded to the destination with the help of the established semantic fields. At each node, the packet is forwarded in the direction in which the field has the steepest gradient until it reaches the destination.

5.1: Development of the Basic Concepts:

Combining Service Discovery and Semantic


The network automatically organizes itself into a multi-dimensional semantic layer to provide basic routing and service discovery functionality to applications. The goal of this layer is to structure the nodes in a way that they form an ordered structure from requester to resources for each attribute. Together with this layer, the data forwarding process “simply results in diffusing” named data packets in these fields. Fields are generated in the

following way: Services actively advertise their profile to the network to describe the service they provide. Service advertisements are broadcasted in the form of positive weighted service descriptions. In the same way, clients periodically announce their interests by sending out a service description of the desired services. These advertisements have negative weights. Each node in the network listens to these periodic announcements. When a node receives an advertisement for the first time, it forwards the advertisement immediately to all its neighbors.

5.2: Semantic QoS Extensions for Field-based Routing and Optimization Techniques

The present module focuses on the semantic QoS specific extensions for field-based routing. The capacity or potential described in the basic system is extended to include QoS metrics depending on the context or the semantic information. Besides the extension of field based routing, one also has to think about the scalability issues of the proposed routing scheme. This component also covers the efforts to build mechanisms able to scale to any network size. Specifically, we have to address the distribution and storage overhead necessary to disseminate routing information. For example, all advertisements are soft state services and clients must re-advertise periodically their services and interests or the entry are deleted after a given timeout at the intermediate nodes. This eliminates the need to explicitly de-register entries. However, periodic re-advertisements are not forwarded directly by the intermediate nodes. Instead, these advertisements are cached locally and forwarded in one single multi-service/multi-interest advertisement.

6. Context-Aware Service

Negotiation in WiMAX using SIP


One of the innovative features of the IEEE 802.16 standard for broadband wireless access is that it provides the ability to specify connections with different QoS characteristics at the MAC layer. Each MAC connection is associated with a Service Flow that describes various QoS aspects such as the connection's minimum reserved and maximum sustained traffic rate. Outgoing traffic is mapped to a particular connection (and thus it's associated Service Flow) through the process of classification: The IEEE 802.16 standard defines a robust QoS


architecture that can serve as the basis for semantic-based service negotiation and reconfiguration. The Base Station can dynamically add/delete service flows, or modify the characteristics of existing flows, depending on environment and context conditions.


Figure 2: Architecture for propagating the context-aware QoS rules through a network.

Figure 2: Architecture for propagating the context-aware QoS rules through a network.

The goal of this component is to integrate signalling mechanisms and link-layer QoS features of IEEE 802.16 protocol for semantic based service negotiation. More specifically, the session layer SIP protocol along with the SDP protocol for describing QoS-related connection parameters, are used as an intermediate between the semantic QoS framework and the IEEE 802.16 protocol's dynamic service signalling infrastructure. An important thing to note is that it is not clear at this point of time whether Subscriber Station initiated dynamic service signalling are implemented in commercial IEEE 802.16 products, since this functionality is defined as optional in the standard. Hence, the proposed method should provide a mechanism for service negotiation regardless of the capabilities of the Subscriber Station. Figure 2 shows a block diagram of the architecture.

7. Conclusions

In this paper, we present a novel architecture for efficient data routing within either wireline or wireless networks. The key idea of the presented method is to exploit audio-visual semantics in order to improve routing performance. Thus, the algorithm “looks” inside the data, i.e., it interprets the content and based on this it take decisions

regarding routing. This way, QoS is maximally guaranteed.


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