Internet DRAFT - draft-wang-tsvwg-qoe-evaluation-has

draft-wang-tsvwg-qoe-evaluation-has



Informational                                                     F.Wang
Internet Draft                           Beijing Institute of Technology
Intended status: Informational                                     Z.Fei
Expires: Nov 26, 2015                    Beijing Institute of Technology
                                                            May 26, 2015

              QoE Evaluation for HTTP Adaptive Streaming 
          draft-wang-tsvwg-qoe-evaluation-has-01.txt

Abstract

   This document describes a method to evaluate the Quality of
   Experience (QoE) of real-time video delivered over HTTP Adaptive
   Streaming (HAS) technology. Not only the end points but also the
   content providers and network operators can acquire the QoE of HAS by
   implementing this method.

Status of this Memo

   This Internet-Draft is submitted to IETF in full conformance with the
   provisions of BCP 78 and BCP 79.

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   This Internet-Draft will expire on Sept 18, 2015.

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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  2
     1.1.  Method of evaluate QoE for HAS . . . . . . . . . . . . . .  2
   2.  Conventions used in this document  . . . . . . . . . . . . . .  3
   3.  Chunk-quality metrics  . . . . . . . . . . . . . . . . . . . .  3
     3.1.  The analysis of Quality metrics  . . . . . . . . . . . . .  3
     3.2.  Metric chosen for QoE evaluation . . . . . . . . . . . . .  3
     3.3.  The chunk-quality tag  . . . . . . . . . . . . . . . . . .  4
   4. Pooling method  . . . . . . . . . . . . . . . . . . . . . . . .  5
   5.  Security Considerations  . . . . . . . . . . . . . . . . . . .  5
   6.  IANA Considerations  . . . . . . . . . . . . . . . . . . . . .  5
   7.  References . . . . . . . . . . . . . . . . . . . . . . . . . .  6
     7.1.  Normative References . . . . . . . . . . . . . . . . . . .  6
     7.2.  Informative References . . . . . . . . . . . . . . . . . .  6
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . .  6

1.  Introduction

   HTTP Adaptive Streaming (HAS) has been used widely to deliver real-
   time video streams, including Apple's HTTP Live Streaming (HLS),
   Microsoft Smooth Streaming (MSS), Adobe's HTTP Dynamic Streaming 
   (HDS), and 3GPP's standardized solution 3GP-DASH [DASH].

   Most HAS protocols have defined Manifest File, i.e. Media 
   Presentation Description (MPD) of 3GP-DASH and Playlist file of HLS,
   which lists the location of various chunks, as well as some other
   informational tags set such as the way the content has been chunked.
   Each chunk is specified by its address and associated informational
   tags set. There will be no change in quality for a single chunk after
   it has been delivered due to the reliability of HTTP, thus the
   quality of each chunk can be measured at the source point before
   multimedia delivering.

   Based on the above analysis, the quality of each chunk can be as a
   tag and embedded into the associated informational tags set of
   Manifest Files. Then the overall QoE for the whole video can be
   obtained by a pooling model which takes the quality of each received
   chunk into consideration.

1.1.  Method of evaluate QoE for HAS
 
   Since, for HAS all Manifest files must be download before the
   multimedia data playing, by capturing and parsing these files,
   quality of each chunk can be get easily. For each playing chunk, its
   corresponding information can be acquired from the HTTP request
   information, because a URL that included in the HTTP request
   information, corresponds to a specific presentation.

   By the above analysis, the quality of a playing chunk can be obtained
   in real time at any point, and the quality for a certain time can be
   predicted through pooling methods which take all the segments within
   the prediction period into consideration. The simplest pooling method
   is averaging over all played chunks. In this draft, we have proposed

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   a liner model which has been demonstrated has a higher accuracy in
   [Pooling method].

2.  Conventions used in this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

   In this document, these words will appear with that interpretation  
   only when in ALL CAPS. Lower case uses of these words are not to be 
   interpreted as carrying RFC-2119 significance.

   A list of acronyms and abbreviations used in this document are
   presented below.

   o HAS: HTTP Adaptive Streaming

   o HLS: HTTP Live Streaming

   o DASH: Dynamic Adaptive Streaming over HTTP

   o QoE: Quality of Experience

   o MOS: Mean of Score

   o PSNR: Peak Signal to Noise Ratio

   o dPSNR: differential PSNR

3.  Chunk-quality metrics

3.1.  The analysis of Quality metrics 

   The quality of each chunk can be measured by subjective or objective
   methods. It is recognized that subjective methods are time and
 
   manpower consuming, which makes these methods hard to utilize in
   practice. The limitation of subjective methods drives the development
   of objective methods. Usually the objective methods can be divided
   into three categories according to the dependency of original video:
   1) full-reference methods (FR, in which need complete original
   content), 2) reduced-reference methods (RR, in which need partial
   information about original content) 3) no-reference methods (NR, in
   which need nothing about the original content).
 
   FR methods are more simple and accurate than RR and NR methods but
   not practical for most delivery scenarios due to their dependence of
   the complete original content. But for HAS delivery scenario, FR
   methods can be used, since the quality of each segment can be
   measured at the source point that with original content available.

 3.2.  Metric chosen for QoE evaluation

    
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   Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) and
   Video Quality Metric (VQM) are three commonly used FR objective
   metrics for quality analysis. But these methods are sensitive to
   video content and often used as a measurement metric for different
   compression versions with a same given original content. The
   sensitivity of these methods are caused by the different spatial-
   temporal characteristics in different video content, which will lead
   to different PSNR range for different content although they have the
   same QoE range.

   In order to solve the above problem, this draft present the concept
   of differential PSNR (dPSNR) based on PSNR. For each segment, a
   suitable presentation is chosen as the benchmark, then the dPSNR of
   all presentations of this segment are obtained by subtracting the
   PSNR value of the benchmark.

   In order to solve the above problem, this draft present the concept
   of differential PSNR (dPSNR) based on PSNR. For each segment, a
   suitable presentation is chosen as the benchmark, then the dPSNR of
   all presentations of this segment are obtained by subtracting the
   PSNR value of the benchmark.

   E.g. if different contents at a content provider are encoded with the
   same bitrate levels set, for each segment, we can choose the
   presentation with the highest bitrate level as the benchmark.

3.3.  The chunk-quality tag

   Manifest files contain the URLs and other informational tags for
   various chunks. In this draft, the following attributes are defined
   and added to the basic Manifest files:

   QUALITY TYPE

   The value is the type of quality metric has been used.

   VALUE

   It denotes the value of quality under the given quality metric which
   can be get from the QUALITY TYPE attribute.

   DESCRIPTION

   It denotes some other information for presentation-quality, e.g. if
   we have chosen dPSNR as the presentation-quality metric, it can
   describe whether a presentation is chosen as a benchmark, if QUALITY
   DESCRIPTION=1 the presentation is a benchmark, otherwise not.

   In this draft, dPSNR has been used as the presentation-quality
   metric. Thus the QUALITY TYPE: dPSNR. The dPSNR value of each
   multimedia presentation can be calculated at the content provider
   point.

   
    
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   A simple example of media play list file for HLS after we define and
   embed the chunk-quality tag #EXT-X-QoE:

   #EXTM3U
   #EXT-X-VERSION: 3
   #EXT-X-TARGETDURATION: 8
   #EXT-X-MEDIA-SEQUENCE: 2680
   #EXT-X-QoE: QUALITY TYPE=dPSNR, VALUE=20, DESCRIPTION=0
   #EXTINF: 7.975, 
   https://priv.example.com/fileSequence2680.ts
   #EXT-X-QoE: QUALITY TYPE=dPSNR, VALUE=18, DESCRIPTION=0
   #EXTINF: 7.941,
   https://priv.example.com/fileSequence2681.ts
   #EXT-X-QoE: QUALITY TYPE=dPSNR, VALUE=15, DESCRIPTION=0
   #EXTINF: 7.975, 
   https://priv.example.com/fileSequence2682.ts


4. Pooling method

   Since many tests have proved that for a certain time, the quality
   perceived by the end users not only depends on mean video quality,
   but also depends much on the segments with the best or worst quality
   that may leave a deep impression to end users, and the occurrence of
   quality switching that will distract viewers' attention. The liner
   model is proposed based on the above analysis, which take more
   influenced factors into consideration.
 
   A simple example to evaluate quality for a certain period is shown as
   follows,

   PMOS=a*mean+b*max+c*min+d*std,

   where a,b,c,d are parameters associated with content type, encoded
   type and prediction period, mean, max, min, std are calculated
   influenced factors, which measure the mean quality, maximum quality,
   minimum quality and standard deviation quality respectively.

5.  Security Considerations

   Since the protocol relies on HTTP Live Streaming, most of the same
   security considerations apply. See section 11 of draft-pantos-
   httplive-streaming-13.

6.  IANA Considerations

   Same IANA considerations of HTTP Live Streaming apply. See section 10
   of draft-pantos-http-live-streaming-13.







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7.  References

7.1.  Normative References

   [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
   Requirement Levels", BCP 14, RFC 2119, March 1997.

   [RFC2616] Fielding, R., Gettys, J., Mogul, J., Frystyk, H.,Masinter,
   L., Leach, P. and T. Berners-Lee, "Hypertext Transfer Protocol --
   HTTP/1.1", RFC 2616, June 1999.

7.2.  Informative References

   [DASH] 3GPP TS 26.247 v12.2.0, "Progressive Download and Dynamic
   Adaptive Streaming over HTTP (3GP-DASH)", Release 12, mar 2014

   [Pooling method] Xiaolin Deng, Liang Chen, Fei Wang "A Novel Strategy
   to Evaluate QoE for Video Service Delivered over HTTP Adaptive
   Streaming", unpublished

Authors' Addresses

   Fei Wang
   Beijing Institute of Technology
   5 South Zhongguancun Street, Haidian District, Beijing, China

   Email: fei_wang@bit.edu.cn
 

   Zesong Fei
   Beijing Institute of Technology
   5 South Zhongguancun Street, Haidian District, Beijing, China

   Email: feizesong@bit.edu.cn





















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