Tsuwei Chen, Mario Gerla, Manthos Kazantzidis, Yuri Romanenko, Ilya Slain

1. Experiments on QoS Adaptation for Improving End User Speech Perception Over Multi-Hop Wireless Networks Tsuwei Chen, Mario Gerla, 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

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

3. lr > TUpper lr > TUpper lr > TUpper lr > TUpper lr  TLower lr  TLower lr  TLower lr  TLower Adaptation for Audio Sources Caption only 8kHz 11kHz 22kHz 44kHz lr:Packet loss rate, TUpper, TLower:Upper and lower packet loss thresholds Goals • Keep connection as long as possible • reduce call setup overhead • Reduce sound quality to keep information quality • Improve sound quality only when information quality is met

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

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

6. Server Player Text to Speech Client Player Caption File Speech Recognition TTS Sync SessionController SessionController TransportController TransportController QoS API QoS API Network Monitor Adapting To Very High Loss: Speech-To-Text and Text-To-Speech Audio File QoS feedback data Wireless Networks QoS feedback

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

8. TestBed Virtual Interfaces 131.179.25.26 131.179.26.26 131.179.27.26 Intermediate node Linux Multihop Routing Server NT/95 Client NT/95 26 subnet 25 subnet 131.179.26.25 131.179.25.175 27 subnet 131.179.27.174 Additional Traffic Linux • 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

9. Experiment Parameters • Speech Encoding • PCM (mono, 8-bit samples, 22Khz) • server on-demand extracts lower bit/sampling rates of 11Khz, 8Khz. • RTP-type packets • 240, 480 and 960 bytes. • Jitter measurement • Rate 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 Recognition • Microsoft Speech Engine • Limited Vocabulary of 1000 words • Voice and Synthesized Speech Synchronization • On time-window (2-sec) granularity • Speed (wpm) adaptation inside window

10. Emulation Experiment Sampling Rate of Audio Transmission follows Available Bandwidth. Available Bandwidth of Emulated Channel

11. No Adaptation • The audio stream is not responsive to the loss rates observed. • Significant quality degradation perceived with increased loss rate

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

13. Packet Size Adaptation • Packet Size adjustment is very effective in reducing loss rates • Packet Size adjustment does not have any audible side effects

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)

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%

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

17. Cellular vs Multihop Standard Base-Station Cellular Networks Instant Infrastructure, Multihop wireless Networks

18. Ad-Hoc Network Characteristics • Instantly deployable • Reconfigurable infrastructure • Node mobility • Heterogeneous nodes • big/small • fast/slow • Heterogeneous traffic • voice, image, video, data • Limited battery power • Multihopping • to save power • overcome obstacles • enhance spatial spectrum reuse

19. Ad-Hoc Network Applications • Disaster Recovery • flood, fire, earthquakes etc • Law enforcement • crowd control, border patrol etc • Search and rescue in remote areas • Sport events, festivals • Ad hoc nomadic, collaborative computing • Indoor network appliances • Sensor networks • Battlefield