2. UCLA CS W ireless A daptive M obility Lab 1 Experiments on QoS Adaptation for Improving End User Speech Perception Over Multi-Hop Wireless Networks Tsuwei Chen, Mario Gerla, Manthos Kazantzidis, Manthos Kazantzidis, Yuri Romanenko, Ilya Slain http://www.cs.ucla.edu/NRL/wireless June 1999 {tsuwei,gerla,kazantz,yuri,ilyas}@cs.ucla.edu Mini Quality Of Service Conference during ICC 99

3. UCLA CS W ireless A daptive M obility Lab 2 Motivation Wireless networks characteristics:Wireless networks characteristics: –high packet loss –highly varied packet delivery time –constantly changing QoS Audio delivery applications characteristics:Audio delivery applications characteristics: –Sensitive to packet loss ~ 20% pkt loss  sharp perceptual quality degradation~ 20% pkt loss  sharp perceptual quality degradation –Sensitive to delay jitter Existing audio/video delivery Internet applicationsExisting audio/video delivery Internet applications –e.g. VIC, VAT –Few are adaptive to changing network conditions –Not suitable for wireless environment

4. UCLA CS W ireless A daptive M obility Lab 3 Adaptation for Audio Sources 8kHz11kHz22kHz44kHz Goals Keep connection as long as possibleKeep connection as long as possible –reduce call setup overhead Reduce sound quality to keep information qualityReduce sound quality to keep information quality Improve sound quality only when information quality is metImprove sound quality only when information quality is met Caption only lr: T Upper, T Lower : lr: Packet loss rate, T Upper, T Lower : Upper and lower packet loss thresholds lr > T Upper lr  T Lower lr > T Upper lr  T Lower

5. UCLA CS W ireless A daptive M obility Lab 4 Adaptive Speech Strategies Options –End-to-End vs network QoS feedback –Source vs network rate/stream adaptation Server Adjustable Parameters: sampling rate sampling rate source encoding source encoding packet size packet size encryption encryption End-to-End ARQ End-to-End ARQ Speech-to-Text Speech-to-Text Client QoS monitoring: packet loss packet loss delay jitter delay jitter pkt noise pkt noise Network QoS Feedback: Feedback: congestion indication congestion indication Bdw advertising Bdw advertising S/N advertising S/N advertising Network Adaptation: layer thinning layer thinning link ARQ link ARQ channel encoding channel encoding internal interference external interference (jamming, environment noise) congestion channel fading security breach..... server client mobility

6. UCLA CS W ireless A daptive M obility Lab 5 Wireless Network UCLA Adaptive Speech Experiment Multihop Testbed client Adjustable Parameters - sampling rate - packet size QoS Monitoring: - packet loss - jitter Audio (UDP) Control (TCP) A d a p t a t I o n S t r a t e g y : Audio source adapts to QoS feedback Increase in Packet loss packet size is reduced sampling rate is reduced Increase in jitter network congested channel noise/interference Piggybacked Text Stream (UDP) server TTS Sync Speech Recognition

7. UCLA CS W ireless A daptive M obility Lab 6 Server Player Session Controller Transport Controller QoS API Client Player Session Controller Transport Controller QoS API data QoS feedback Network Monitor Audio File Speech Recognition Caption File Text to Speech Wireless Networks Adapting To Very High Loss: Speech-To-Text and Text-To-Speech TTS Sync

8. UCLA CS W ireless A daptive M obility Lab 7 Caption Embedding Scheme 0 sec2 sec Voice file (.wav) Question: When you are driving @ at fifty-five miles per hour … 0 sec 2 sec …….. Caption file (.cpt) For reliable caption delivery, each 2-second segment of text is replicated in several packets. #0#1#2#3#4 Wireless packets

9. UCLA CS W ireless A daptive M obility Lab 8 Packets are forwarded using routing tables In order to force multihop in confined environment, nodes are partitioned into subnets WaveLan I, CSMA/CA, 915Mhz, 2Mbps, 700ft (200m) open, 100ft (30m) closed Multihop Routing Virtual Interfaces 131.179.25.26 131.179.26.26 131.179.27.26 131.179.27.174 131.179.25.175 131.179.26.25 26 subnet 25 subnet 27 subnet TestBed Server NT/95 Intermediate node Linux Client NT/95 Additional Traffic Linux

10. UCLA CS W ireless A daptive M obility Lab 9 Speech EncodingSpeech Encoding –PCM (mono, 8-bit samples, 22Khz) server on-demand extracts lower bit/sampling rates of 11Khz, 8Khz.server on-demand extracts lower bit/sampling rates of 11Khz, 8Khz. RTP-type packetsRTP-type packets 240, 480 and 960 bytes.240, 480 and 960 bytes. Jitter measurementJitter measurement Rate Adaptation StepsRate Adaptation Steps –Single Action For every QoS Feedback IF loss > MAX_LOSS_THRESHOLD DOWNGRADE rate, payload IF jitter > MAX_JITTER_THRESHOLD DOWNGRADE rate IF loss < MIN_LOSS_THRESHOLD UPGRADE rate, payload Speech RecognitionSpeech Recognition –Microsoft Speech Engine –Limited Vocabulary of 1000 words Voice and Synthesized Speech SynchronizationVoice and Synthesized Speech Synchronization –On time-window (2-sec) granularity Speed (wpm) adaptation inside windowSpeed (wpm) adaptation inside window Experiment Parameters

11. UCLA CS W ireless A daptive M obility Lab 10 Emulation Experiment Available Bandwidth of Emulated Channel Sampling Rate of Audio Transmission follows Available Bandwidth.

12. UCLA CS W ireless A daptive M obility Lab 11 No Adaptation The audio stream is not responsive to the loss rates observed. Significant quality degradation perceived with increased loss rate

13. UCLA CS W ireless A daptive M obility Lab 12 Sampling Rate Adaptation The rate used for the audio coding is reduced when loss rates are high. The sampling rate directly follows loss rate trends resulting in better perception quality

14. UCLA CS W ireless A daptive M obility Lab 13 Packet Size Adaptation Packet Size adjustment is very effective in reducing loss rates Packet Size adjustment does not have any audible side effects

15. UCLA CS W ireless A daptive M obility Lab 14 Adaptation on Both Packet Size and Sampling Rate Adaptation on both packet size and sampling rate results in the lowest loss rates (best perception quality)

16. UCLA CS W ireless A daptive M obility Lab 15 Adapting To Very High Loss using SR/TTS At very high loss rates, beyond a threshold, Speech Recognition is applied at the server and Text-To-Speech synthesis at the client side. The speech is recognized at packet loss rates up to 99%

17. UCLA CS W ireless A daptive M obility Lab 16 Conclusions Perceived speech quality is substantially enhanced by payload size and rate adaptationPerceived speech quality is substantially enhanced by payload size and rate adaptation Recognizable speech is delivered in extreme conditions by Text-To-Speech translationRecognizable speech is delivered in extreme conditions by Text-To-Speech translation Work in progress Real/Synthetic Speech synchronizationReal/Synthetic Speech synchronization –explicit relative byte-to-word mapping Production of Speech CODEC tracesProduction of Speech CODEC traces –CELP, SBC, A/u-Law, DSP TSpeech, GSM 6.10, G723, MP3 etc. Large scale network emulation experimentsLarge scale network emulation experiments –using GlomoSim platform Adaptive Video-On-DemandAdaptive Video-On-Demand Network Feedback (QoS routing)Network Feedback (QoS routing) Multicast Stream AdaptationMulticast Stream Adaptation

18. UCLA CS W ireless A daptive M obility Lab 17 Cellular vs Multihop Standard Base-Station Cellular Networks Instant Infrastructure, Multihop wireless Networks

19. UCLA CS W ireless A daptive M obility Lab 18 Ad-Hoc Network Characteristics Instantly deployableInstantly deployable Reconfigurable infrastructureReconfigurable infrastructure Node mobilityNode mobility Heterogeneous nodesHeterogeneous nodes –big/small –fast/slow Heterogeneous trafficHeterogeneous traffic –voice, image, video, data Limited battery powerLimited battery power MultihoppingMultihopping –to save power –overcome obstacles –enhance spatial spectrum reuse

20. UCLA CS W ireless A daptive M obility Lab 19 Ad-Hoc Network Applications Disaster RecoveryDisaster Recovery –flood, fire, earthquakes etc Law enforcementLaw enforcement –crowd control, border patrol etc Search and rescue in remote areasSearch and rescue in remote areas Sport events, festivalsSport events, festivals Ad hoc nomadic, collaborative computingAd hoc nomadic, collaborative computing Indoor network appliancesIndoor network appliances Sensor networksSensor networks BattlefieldBattlefield