Intelligent and Adaptive Middleware to Improve User-Perceived QoS in Multimedia Applications Pedro M. Ruiz, Juan A. Botia, Antonio Gomez-Skarmeta University of Murcia Terena Networking Conference 2004 Rhodes, June 2004

2 Drivers for adaptive applications E2E QoS requires local resource management Terminals are heterogeneous and media adaptation is needed Network conditions are unpredictably changing and not under control (e.g. Ad hoc nets, PLC networks, etc.) QoS in terms of bandwidth and delay cannot be guaranteed just with network-layer QoS mechanisms In these cases, user-perceived QoS can be improved using applications being able to adapt to: –Network conditions –QoS Violations –Shortage of local resources (eg. CPU, Memory, etc.) –User preferences

3 Adaptive Applications

4 Video Quality vs. Bandwidth Non-linear Perception 1190 Kbps 300 Kbps 210 Kbps 140 Kbps 70 Kbps 50 Kbps 30 Kbps 10 FPS, SQCIF both for MJPEG and H.263

5 Towards user-awareness... Traditional approaches based on profiles –Simple and easy to implement –Usually are not fine-grained enough –Are not able to capture the perceptual preferences Adaptations based on low level parameters (e.g. Bandwidth, packet losses, etc) –Do not really consider user preferences –Perceptual QoS is not linearly related to those low level metrics Previous works focused on evaluating the impact of each parameter on the user perception –There is not a real model of the user, particularly when multiple parameters can be tuned simultaneously (e.g. codecs, frame rates, etc.)

6 Architecture for Multimedia Adaptive Applications TypeSeq #Loss %DelayUser prefEstimated BW Audio Codec Video Codec Frame Rate Size Quantization...

7 Basic Adaptation Logic

8 Why applications aware of the user-perceived QoS? There are many ways to adapt data-rates to the available bandwidth –Audio & Video Codecs –Video Quantization factor –Audio sampling rate –Video frame rate –Video size –Component selection –Buffering Not trivial to select a new combination of settings satisfying the users –Reduce frame size?, reduce frame rate?, change codec? Traditional adaptive applications improve user-perceived QoS but they offer sub-optimal solutions Adaptive applications should be able to deal with the user perception of QoS! Which combination would be preferred by the user?

9 Our proposal for user awareness Use of machine learning techniques to help at modeling the user perceived QoS –Number of possible combinations of application settings is big enough! –Perceptual QoS may change from one indivudual to another and it is extemely complex to be analitically modelled –A “black box” model may resemble the user- satisfaction without needing to understand the complex processes involved in user perception

10 Modelling user-perceived QoS Difficult to model, due to the subjective aspects involved We apply a rule induction machine learning algorithm over learned data BW33, 56, 88, 128, 384Kb/s ACOD PCM, G.711u, G722, GSM VCOD MJPEG, H263 FSIZE CIF, QCIF, 160x128 Quant 5, 10, 15, 30, 60 FPS Loss% % Score1..5 (according to MOS) Initial data-set (864 entries) SLIPPER algorithm with t=5 Set of rules representing user-perceived QoS

11 if matchConfidence { [QFVIDEO >= 60, VIDCOD = MJPEG, FSIZE = QCIF, LOSS = 6] -> [AUDCOD = GSM, BW >= 80, QFVIDEO >= 30, FSIZE = QCIF, FPS [AUDCOD = GSM, BW >= 128, LOSS = 0, QFVIDEO >= 30, FPS >= 3, VIDCOD = MJPEG] -> [] -> } > 0 then 5 else if matchConfidence { [BW >= 384, QFVIDEO >= 40, FSIZE [QFVIDEO >= 30, VIDCOD = MJPEG, LOSS =80] -> [FSIZE = CIF, QFVIDEO >= 30, LOSS = 80] -> [] -> } > 0 then 4 else if matchConfidence { [LOSS >= 30] -> [QFVIDEO [LOSS >= 16, FPS [] -> } > 0 then 1 else if matchConfidence { [LOSS >= 16] -> [QFVIDEO [FSIZE = 160X128, QFVIDEO [] -> } > 0 then 2 else 3 Rules Generated by SLIPPER Some of the lessons learnt from rules Higher FR => higher QoS but user’s prefer lower FR (not below 4 FPS) provided that the video is bigger In most cases PCM audio is not required. The bandwidth savings can be used to improve other components Audio QoS has greater impact Etc..

12 Empirical Results Scenario –Real MMARP-based ad hoc network –Path specifically selected to guarantee variability Application –ISABEL-Lite with extensions Trials –Traditional multimedia application –Adaptive multimedia application

13 Total Losses

14 Histogram audio/video loss-rate

15 User’s Mean Opinion Scores

16 Conclusions and Future Work Adaptive applications have demonstrated to be effective in wireless and mobile scenarios The machine learning user’s modelling has shown to be effective Applications aware to the user-perceived QoS have demonstrated to offer to better satisfy user’s QoS expectations in a real ad hoc wireless networks Optimization on the triggering of the adaptation have demonstrated Future work include among others –Reinforcement learning inside the terminal –Combination with user profiling mechanisms

Intelligent and Adaptive Middleware to Improve User-Perceived QoS in Multimedia Applications Pedro M. Ruiz, Juan A. Botia, Antonio Gomez-Skarmeta University of Murcia Terena Networking Conference 2004 Rhodes, June 2004