Home > Articles > Software Development & Management

Quality of Service for Internet Multimedia: a General Mapping Framework

Continuous media applications have exceptionally stringent QoS requirements, and QoS for multimedia will remain a challenge well into the future. In this chapter from Quality of Service for Internet Multimedia, the authors present a futuristic QoS mapping framework.
This chapter is from the book

3.1 Introduction

Ongoing research efforts regarding service differentiation can be classified into two approaches: absolute differentiated service and relative differentiated service, as mentioned in Section 2.5.

Absolute differentiated service assures an admitted user of the promised performance level of the DiffServ classes, independent of the traffic status of the network. Therefore, such an approach is useful for applications that require strict QoS guarantees. On the other hand, realization of absolute differentiated service requires stringent admission control.

On the application side, much intelligence can be added to application-layer protocols nowadays. In the case of a multimedia application, the transmission rate can adapt to network congestion through, for example, a choice of different compression rates. Regardless of absolute or relative differentiation, the DiffServ framework can best be utilized by implementing some intelligence at the boundary nodes of the DiffServ domain. As CM applications become more network-adaptive, it appears that many will perform well with relative differentiated service, even though the network performance guaranteed by relative DiffServ is not as solid as that guaranteed by absolute DiffServ. Except for several conversational CM applications (e.g., Internet telephony, including video conferencing), the majority of networked CM applications are tolerant of occasional delay/loss violations. Thus, they do not require tight delay/loss bounds, which can be better provided by DiffServ premium service (PS).1 For streaming video applications, encoding/decoding is more resilient to the loss rate and delay fluctuations, and thus the capability of relative service differentiation seems adequate for the streaming video applications on which we will focus.

With an appropriate pricing rule, the method of exploiting relative differentiation for CM applications seems to be an important issue for successful cooperation between the differentiated service and the CM applications. Thus, in this chapter, we propose a subscription-based pricing model for differentiated service quality among DS levels2 specified in the SLA, under which there is a futuristic framework for QoS mapping between practically categorized packet video and a relative DiffServ network employing a unified priority index and an adaptive packet-forwarding mechanism. In this framework, the video application at the source grades the chunks of its content by certain indices (i.e., categories for packets) according to their importance in end-to-end QoS (e.g., in terms of loss probability and delay). Since these indices reflect the desired service preference of a packet compared with others in fine granularity, we denote them with an RPI, which is further divided into a relative loss priority index (RLI) and a relative delay priority index (RDI).

Next, QoS control takes place through the assigning of an appropriate DS level to each packet, a process that we call QoS mapping. Using the RPI association for each packet, an efficient (i.e., content-aware) mapping can be coordinated either at the end-application or at the boundary node. Note that the efficiency of QoS mapping for relative differentiation is dependent on the persistence of the contracted (advertised) quality differentiation over different time scales in the presence of traffic fluctuation. That is, the packet-forwarding mechanisms (e.g., queue management and packet scheduling) of DiffServ needs to provide the target performance differentiation persistently over time. (In Chapters 5 and 6, we will discuss how to seek persistent service differentiation and how such persistence improves end-to-end quality through QoS mapping.) With a relative DiffServ network providing consistent service differentiation persistently over time, CM applications, including streaming video, can be built more reliably and for less cost.

This chapter presents a relative service differentiation framework connecting CM applications, especially streaming video applications, through the proposed RPI. The chapter addresses the following issues: (1) a relative priority-based, per-packet video categorization in terms of delay and loss; and (2) an optimal (or effective) QoS mapping between application categories and DS levels under the pricing cost constraint of the relative service differentiation network. Actually, this framework belongs to joint source/channel coding, and more specifically to the UEP technique. Commonly, UEP enables prioritized protection for source layers (e.g., layered streams of video). It can be realized at the transport end with different levels of FEC and/or ARQ for each layer [91], [92], [93], [94]. However, to the best of our knowledge, no UEP approach has addressed the issue of using packet-level, fine-grained prioritization of the proposed RPI instead of layered protection. For a DiffServ network especially, only layered prioritization in an absolute differentiation sense has recently been proposed in [95], utilizing the video object layer of MPEG-4 and a different packet-discarding mechanism.

The rest of this chapter is organized as follows. The proposed QoS mapping framework is described in Section 3.2. Video categorization with RPI according to several criteria is examined in Section 3.3 for the case of ITU-T H.263+ video [96]. By investigating the error-resilient version of the H.263+ stream, the RPI is assigned so that different video packets can be tied to the relative loss rate/delay differentiation of DiffServ networks. Then, optimal QoS mapping guidance is presented for a certain packet loss rate and given cost curve according to the DS level. Performance assessments using the random loss pattern from a two-state Markov model for each DS level and H.263+ video are given in Section 3.5, where the implications of experimental results are also discussed. Finally, concluding remarks and anticipated future research efforts are given in Section 3.6.

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information

To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.


Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.


If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information

Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.


This site is not directed to children under the age of 13.


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information

If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information

Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents

California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure

Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact

Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice

We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020