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2018 ITU Kaleidoscope Academic Conference




           the system performance particularly in  handling the real-  at client. The server adjusts video resolution based on the
           time applications like video streaming. The on-policy   client feedback dynamically, the degradation of video
           algorithms such as Sarsa and Double Sarsa [15] prove to be   quality at receiver is observed due to scaling down of the
           a better fit here because they learn the action-values at each   original content at the server. The proposed solution could
           step, depending solely on the states visited and action taken.   be standardized considering its practical use in supporting
           When rewards are stochastic, Double Sarsa adds significant   adaptive streaming of  video in IP network over  wireless
           amount of stability in the learning process at minor increase   systems.
           in computational cost, while providing a higher return in an
           on-policy algorithm.                                           2.  PROPOSED SYSTEM

           In assessing the quality of adaptive audio-visual streaming   2.1  System Architecture
           over    reliable   transport,  the    International
           Telecommunication    Union’s    Telecommunication  The proposed framework design imitates the client server
           Standardization Sector (ITU-T) has recommended a video   model where the server’s activity gets simplified on the cost
           quality estimation model and tool in ITU-T P.1203.1 [16],   of client’s expanded observation and analysis process. It is
           which is a parametric bitstream-based quality assessment   implemented on top of HTTP where the live (or stored)
           method. This model is intended for client-side monitoring   video is  streamed  from the server to the client connected
           of encrypted/non-encrypted HTTP/TCP  based video  on   through a 4G  wireless  network. The  media content is
           demand VoD / live streaming services. Mode-2 of operation   encoded progressively  utilizing ITU-T H.264 video codec
           defined in P.1203.1 is intended for non-encrypted  media   [19] and then streamed to the client. Once the streaming
           and requires an input of  meta-data and up-to 2% of  the   starts, at the client side the proposed algorithm analyses the
           media stream with a medium complexity.             quality of the streamed  media along  with the  network
                                                              conditions in order to calculate the decision parameters to
           In this  work,  we propose a  new algorithm based on RL   be sent as feedback to the server. The server analyses the
           approach, Double Sarsa to improve the quality of a live   feedback and adjusts the video quality to deliver the
           streaming video. In Double Sarsa, two estimates of the   maximum achievable QoE that can be supported by the
           action-value Q(s, a) are decoupled and updated against each   currently available network bandwidth.
           other in request to enhance the rate of learning in a domain
           with a stochastic reward system. The system is       Table 1– Test parameters as per ITU recommendations
           characterized by a set of states and actions where the best
           possible action is taken from the current state through  a   Standards  Parameters   Metrics
           gradual learning process. It then calculates the reward in
           terms of Mean Opinion Score (MOS) using the ITU-T               Transmission   Errors with packet loss
           P.1203.1 framework and determines the resulting state of
           the system. Two exploration policies: softmax and ε-greedy       Frame rate   5fps to 30fps
           are used separately to find the future action to be taken  ITU-T              H.264/AVC (MPEG-4
           which is sent as feedback to the server and finally the Q-  J.247  Video codec   part10),VC-1,Windows
           matrix is updated. In this approach the adaptation problem                    Media9, Real Video
           is expressed as an optimization process with their proposed                   (RV10), MPEG-4 Part 2
           internal QoE goal function.
                                                                            Temporal     Maximum of 2 seconds
           In analyzing the performance of the proposed system, the           errors
           Double Sarsa based quality adaptation algorithm  is              Input video   20 seconds
           implemented independently  with  softmax policy and  ε -           length
           greedy, and the performance is compared with an existing   ITU-T              240p: 75-150 kbps
           QoE driven strategy  with future information [17].  The   P.1203.1  Video     360p: 220-450 kbps
           algorithms are implemented  on the top (OTT)  of HTTP            resolution /   480p: 375-750 kbps
           while 4G  wireless network are used to establish                   bitrate    720p: 1050-2100 kbps
           connectivity between client and server. The video                             1080p: 1875-12500 kbps
           encoding/decoding  were carried out dynamically in
           accordance with test parameters defined in ITU-T J.247 [18]
           and ITU-T P.1203.1 [16] as listed in Table 1.      2.2  Server Side Functions

           The decoded video sequences at the receiver  were    The server at first obtains the live  media content or the
           compared with the original video transmitted by the server   location of the stored video in memory and sets the media
           for quality evaluation during experimentation. Full   URL. It then uses the Java framework for the VLC media
           Reference (FR)  video quality  metrics  namely Peak Signal   player (VLCJ) to set the appropriate encoding parameters to
           to Noise Ratio (PSNR), Structural Similarity (SSIM),   ensure continuous streaming of the video. The media player
           Multi-Scale SSIM (MS-SSIM), and Video Quality Metrics   object is initialised and the  streaming begins through the
           (VQM) are used to evaluate the quality of streaming video   specified HTTP port.  It then waits for the client’s feedback





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