Motivations (I) - WiFi-enabled devices: mobile phones, laptopos, PDAs
- VoIP applications: Skype, Yahoo Messenger, MSN Messenger, Linphone…
However, providing good voice quality is a challenging task due to several factors
Motivations (II) Some are out of the control of a single STA… - wired part of the e2e connection
- Round trip delay, packet dropping in congested router,
- wireless part of the e2e connection
… while others are under control of the STA - application level
- voice codec, playout buffer scheme, error concealment techniques,…
- MAC/PHY level
Aim of the work Goal: providing good quality of VoIP over WiFi under varying connection conditions Method: Adaptive Parameters Optimization Scheme - Cross-layer iterative adaptation of system parameters
Basic idea: estimate, foresee, select - Estimate factors that you cannot control
- current system state (focused on wireless part only)
- Foresee what you would get by acting on the factors that you can control
- QoS for different (MAC) parameters setting by using a mathematical model of the system
- Select the setting that maximizes QoS
Features: non cooperative, standard compliant, modular
APOS architecture
Parameters vectors (, ) Vectors & include all controllable parameters We distinguish between fixed parameters () - Parameters that hardly change in time
- voice codec, playout delay, packet aggregation level, wired-path latency,…
- …or that cannot be changed preserving std compliancy
- contention window, transmission power, …
And tuneable parameters: =[rmax,R] We optimize over vector only though APOS can potentially apply to all the controllable parameters!!!
APOS architecture
System state vector () System state vector shall… - summarize the collective effect of factors that cannot be controlled
- be almost invariant to single STA parameters setting
We set =[Pcoll, TB, SNR] where - Pcoll: Collision probability
- TB : Channel busy period
- SNR: Signal to Noise Ratio
Pcoll and TB can be directly obtained from MAC counters/measuraments - standard management information base (MIB)
- NTs: # ack.ed MSDU tx by the STA
- NTf: # non ack.ed MSDU tx by the STA
- NRs: # received data frames with valid FCS
- NRf: # received data frames with invalid FCS
- channel occupancy statistics seen by the STA
MSE: Medium State Estimate SNR can be determined from - MAC counters
- + basic probability properties:
APOS architecture
WLM: Wireless Link Model WLM feeds the QEB with the expected packet loss rate (Ploss) and average end to end delay (Te2e) at the varying of the <> input vectors We assume that wired-path delay and losses are negligible - however, the model can accommodate these factors by using, for instance, RTCP reports
Te2e is mainly due to the wireless link delay and the playout buffer Ploss is given by frame dropped by the 802.11 card and frames arriving after their playback time Assuming that the buffering time buff is given, the WLM outputs depend on the wireless link losses and delay only!
Wireless link delay Goal: estimate mean ms, and variance s2 of the system time - Time taken by a voice packet to cross the wireless link
Method: model the wireless link as a D/G/1 queue-server system where - customers MPDUs generated by the upper layers
- arrival rate frame-generation rate of the voice codec (fixed)
- server MAC entity
- service time y time taken by the MAC entity to process a MPDU (either delivering it to the peer unit or dropping it after rmax unsuccessful tx attempts)
- We assume {yj} are i.i.d r.v. also independent of the arrival process
Complete statistical analysis is available in the literature [Servi-’86] but requires roots search for complex polynomial! - not suitable for portable devices (as cel. phones)
We prefer to relinquish the complete statistic in favour of a simpler (though less accurate) estimate of first and second order delay statistics…
Service time statistics (1/2) Given [Pcoll,TB,SNR] , [R,rmax] and [L,...] the service time is given by Working this expression we get* the 1st, 2nd and 3rd-order moments of y - my=E[y]
- My=E[y2]
- My(3)=E[y3]
Service time statistics (2/2)
Wireless link delay - x: number of customers (MPDUs) in the system at the arrival epoch
- y’: residual service time of the (possible) customer in service at the arrival epoch
- H(x) : Heaviside function
taking expectations we finally get - statistical mean (ms):
- statistical power (Ms):
- = my = Pr[x > 0] (load factor)
- whereas, applying renewal theorem, we get:
Packet losses Losses due to the wireless link - Pbuff & delay jitter s are related through the Chebyshev bound*
- Hence, Pbuff can be estimated as
Overall losses
APOS architecture
QEB: Quality Evaluation Block The QEB implements the utility function Q proposed in [Boutr.03] - inspired by the E-Model
- ITU-T recommendations G.107 and G.113
R=Q(vcodec, Ploss,Te2e) R can be mapped to Mean Opinion Score (MOS)
APOS architecture
APO: Adaptive Parameters Optimization - MOSopt=0
- get =[Pcoll, TB, SNR] from MSE
- for =[rmax,R] in parameter space do
- compute [Ploss,Te2e]=WLM()
- compute MOS = Q(Ploss,Te2e)
- If MOS > MOSopt,
- endif
- endfor
endo
Model Validation
Results Theoretically optimal setting in the space Optimal R and rmax depend on both Pcoll, - with low Pcoll, rate adaptation can be more aggressive
- with high SNR, rmax strongly depend on Pcoll
Results Parameters optimization permit to maintain good MOS in many situations - Red lines: std param.s setting
- Black lines: APOS
Conclusions APOS framework is a stand-alone, std compliant solution - mathematical model based on of network status information locally available in commercial devices
APOS enhances VoIP performance by cycling over three steps - 1) estimate the contention level and quality of the wireless medium
- 2) model the effect of parameters tuning on e2e QoS
- 3) select the best configuration for the current medium status
Optimization can be performed on any subset of tuneable parameters - We reported only R & rmax optimization, but we also tested playout buffer, voice codec and so on...
Although parameters might be tuned independently one another (as done by today rate adaptation algorithms), joint optimizing of multiple parameters yield much better results! APOS scheme is specialized to voice application, though the general framework might be adapted to other QoS services
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