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ACE: An Analytical Solution to Video Streaming in Mobile Ad hoc Networks Jeremiah Deng Information Science / Telecommunications Programme University of Otago September 25, 2014 Acknowledgement I Dr. Yuwei Xu I Co-supervisors: Prof. Martin Purvis, Dr. Mariusz Nowostawski Outline Introduction Video Streaming ACE: Modeling Simulations Admission Control Mobility Support Outline Introduction Video Streaming ACE: Modeling Simulations Admission Control Mobility Support Background I Wireless devices are everywhere Background I Wireless devices are everywhere I Mobile phones, tablets, wrist-watches, sensors, tags ... Background I Wireless devices are everywhere I I Mobile phones, tablets, wrist-watches, sensors, tags ... Wi-Fi, Bluetooth, ZigBee ... Background I Wireless devices are everywhere I I I Mobile phones, tablets, wrist-watches, sensors, tags ... Wi-Fi, Bluetooth, ZigBee ... Mobile ad hoc networks can be established on the fly: Background I Wireless devices are everywhere I I I Mobile phones, tablets, wrist-watches, sensors, tags ... Wi-Fi, Bluetooth, ZigBee ... Mobile ad hoc networks can be established on the fly: I No pre-existing infrastructure required Background I Wireless devices are everywhere I I I Mobile phones, tablets, wrist-watches, sensors, tags ... Wi-Fi, Bluetooth, ZigBee ... Mobile ad hoc networks can be established on the fly: I I No pre-existing infrastructure required Multi-hop wireless links Background I Wireless devices are everywhere I I I Mobile phones, tablets, wrist-watches, sensors, tags ... Wi-Fi, Bluetooth, ZigBee ... Mobile ad hoc networks can be established on the fly: I I I No pre-existing infrastructure required Multi-hop wireless links Support mobility Background I Wireless devices are everywhere I I I Mobile ad hoc networks can be established on the fly: I I I I Mobile phones, tablets, wrist-watches, sensors, tags ... Wi-Fi, Bluetooth, ZigBee ... No pre-existing infrastructure required Multi-hop wireless links Support mobility Many application: Background I Wireless devices are everywhere I I I Mobile ad hoc networks can be established on the fly: I I I I Mobile phones, tablets, wrist-watches, sensors, tags ... Wi-Fi, Bluetooth, ZigBee ... No pre-existing infrastructure required Multi-hop wireless links Support mobility Many application: I Personal area networking: entertainment, health monitoring ... Background I Wireless devices are everywhere I I I Mobile ad hoc networks can be established on the fly: I I I I Mobile phones, tablets, wrist-watches, sensors, tags ... Wi-Fi, Bluetooth, ZigBee ... No pre-existing infrastructure required Multi-hop wireless links Support mobility Many application: I I Personal area networking: entertainment, health monitoring ... Military environments Background I Wireless devices are everywhere I I I Mobile ad hoc networks can be established on the fly: I I I I Mobile phones, tablets, wrist-watches, sensors, tags ... Wi-Fi, Bluetooth, ZigBee ... No pre-existing infrastructure required Multi-hop wireless links Support mobility Many application: I I I Personal area networking: entertainment, health monitoring ... Military environments Civilian environments: e.g. taxi cab network, boats, aircraft Background I Wireless devices are everywhere I I I Mobile ad hoc networks can be established on the fly: I I I I Mobile phones, tablets, wrist-watches, sensors, tags ... Wi-Fi, Bluetooth, ZigBee ... No pre-existing infrastructure required Multi-hop wireless links Support mobility Many application: I I I I Personal area networking: entertainment, health monitoring ... Military environments Civilian environments: e.g. taxi cab network, boats, aircraft Emergency operations: search-and-rescue Challenges I Limited wireless transmission range I Broadcast nature of the wireless medium I Packet losses due to transmission errors I Mobility-induced route changes I Mobility-induced packet losses I Battery constraints I Ease of snooping on wireless transmissions (security hazard) Reactive Routing as a Solution I Find a route only when transmission is needed I Controlled flooding of Routing Request (RREQ) and Routing Reply (RREP) frames I Route time-out in case of routing errors I E.g.: Ad hoc On-Demand Distance Vector (AODV) AODV in Action I Nodes propagate Source’s RREQ until reaching Destination I Nodes set up backward links upon hearing RREQ I Destination propagates RREP back to Source I Nodes set up forward links from RREP; Route now formed. Outline Introduction Video Streaming ACE: Modeling Simulations Admission Control Mobility Support Video Streaming Like many other network applications, video streaming relies on the layered TCP/IP stack: I Application sends/receives video packets in continuous flows. I Null transport layer: no flow control or error control I Network layer handles routing I Technologies (Ethernet, Wi-Fi etc.) deal with transmission. Video Streaming in a MANET I Neighbour nodes contend for medium access when transmitting. I Channel quality changeable and subject to weather conditions I Noise and interference may cause transmission errors and retransmission attempts. I Mobility can cause routing disruptions. I Also, data rates (‘bandwidth’) are limited. / Factors related multiple layers lead to worse streaming performance. I Solutions: shortest path (AODV), link quality (ETX), load-balancing (ALARM, NLR), location-assisted (SPEED) ... Outline Introduction Video Streaming ACE: Modeling Simulations Admission Control Mobility Support Queueing Model: Jackson Network of M/M/1/K interference K ...... inflow outflow λ λ µ d reTx I We assume each node can be modelled as an M/M/1/K queueing system with I I I I Poisson arrival process Processing time in exponential distribution System capacity limited to K packets The network is modelled as a Jackson network where queueing models can be analysed individually. M/M/1/K Analysis I State transition diagram I λ: arrival rate, µ: processing rate λ Utility ρ = < 1 µ Steady state solutions: I I I K X pn = 1 n=0 I λ pn = ( )n p0 , 0 ≤ n ≤ K µ M/M/1/K Results I Probability that an arriving customer is rejected is simply pK . I Rejection rate is therefore λd = pK λ. I Time in system T = I 1 µ − λ + λd The expected load Q Q = = ρ (K + 1)ρK +1 − 1−ρ 1 − ρK +1 λ2 µK + K λK +2 − λK +2 − K µλK +1 . µK +2 − µK +1 λ − µλK +1 + λK +2 Our Problem interference K ...... inflow outflow λ λ µ d reTx I In reality, we know λ, but not µ ... I We do know Q (Q ≤ K ), and λd I The question: how to estimate µ, so that the processing time for each node can be derived. The Solution I Re-checking on the packet dropping rate λd = λPK = we have µK +1 = λK +1 µ − λK +2 , µK +1 − λK +1 λK +1 µ − λK +2 + λK +1 . λd The Solution I Re-checking on the packet dropping rate λd = λPK = we have µK +1 = I λK +1 µ − λK +2 , µK +1 − λK +1 λK +1 µ − λK +2 + λK +1 . λd Re-introducing Q: 2λQ λ2 Q λ2 Q 3 3 (µ −λ )+(K − )(µ2 −λ2 )+( − +λ−K λ)(µ−λ) = 0. λd λd λd λd The Solution I Re-checking on the packet dropping rate λd = λPK = we have µK +1 = λK +1 µ − λK +2 , µK +1 − λK +1 λK +1 µ − λK +2 + λK +1 . λd I Re-introducing Q: I 2λQ λ2 Q λ2 Q 3 3 (µ −λ )+(K − )(µ2 −λ2 )+( − +λ−K λ)(µ−λ) = 0. λd λd λd λd Since µ > λ, we have a simplified quadratic equation Q 2 Qλ λ2 µ + (K − )µ + (λ − ) = 0. λd λd λd The Solution I Re-checking on the packet dropping rate λd = λPK = we have µK +1 = λK +1 µ − λK +2 , µK +1 − λK +1 λK +1 µ − λK +2 + λK +1 . λd I Re-introducing Q: I 2λQ λ2 Q λ2 Q 3 3 (µ −λ )+(K − )(µ2 −λ2 )+( − +λ−K λ)(µ−λ) = 0. λd λd λd λd Since µ > λ, we have a simplified quadratic equation I Q 2 Qλ λ2 µ + (K − )µ + (λ − ) = 0. λd λd λd Hence the solution: s λ2 − λd λ λd K − Qλ 2 λd K − Qλ µ= + . − Q 2Q 2Q ACE Routing Metric I Analytical Capacity Estimation (ACE) of a node defined as ∆ = µ − λ + λd I It is the reciprocal of “time in system” for an M/M/1/K I Smoothed, local routing cost at a node n: (n) Ci I (n) = αCi−1 + (1 − α) 1 (n) ∆i Routing metric for a potential route Ω X (n) Ctotal = Ci n∈Ω I Our goal is to find a route with the minimal C. ACE Protocol in a Nutshell 1. Source node floods out RREQ with a metric field and listens to RREP; 2. Intermediate node adds its ACE value to the RREQ metric and propagates it further; sets up reverse links; I RREQ with metrics larger than the best will be dropped; otherwise update the best with current metric. 3. Destination node copies RREQ metric into RREP and sends it back via reverse links through intermediate nodes; I First RREQ arrival starts a timer; time-out to send back RREP with the best metric. 4. Intermediate nodes and Source receive RREP; set up forward links. 5. Data packets can now follow the route formed by forward links. Outline Introduction Video Streaming ACE: Modeling Simulations Admission Control Mobility Support Simulation Settings I Network Simulator 2 (NS-2) + EvalVid in an IEEE 802.11b/g networks of grid topologies: 5 × 5, 7 × 7, 9 × 9, 15 × 15 Parameters Distance between two neighbours Antenna Type Standard Transmission Range Transmission Rate Packet Size Queue Size Video Format Duration Number of Streams Minimum number of hops Values 150m Omnidirectional 802.11b 250m 11 Mbits/s 1024 bytes 50 packets per node H.264/MEPG4 29 ∼ 66s 3∼7 4 Poisson Inflow / Outflow Histogram with Poisson PDF 0.020 Probability 0.000 0.010 0.020 0.000 0.010 Probability 0.030 0.030 Histogram with Poisson PDF 160 180 200 220 240 160 200 220 240 240 220 200 160 180 Sample Quantiles 220 200 180 160 Sample Quantiles 180 number of outward packets 240 number of incoming packets 160 180 200 Theoretical Quantiles 220 240 160 180 200 Theoretical Quantiles 220 240 Chi-Square Test Results For video ‘Highway’: Node N30 N36 N31 DF 6 5 6 Inflow χ2 12.1596 7.3989 11.1698 DF 6 6 6 Outflow χ2 12.1198 10.6659 10.9301 For video ‘Grandma’: Node N34 N41 N49 DF 5 5 5 Inflow χ2 5.2467 4.3583 1.2288 Outflow DF χ2 5 5 5 1.4930 6.0237 4.3098 Our assumption on Poisson traffic stands on the significance level of α = 0.05. Comparison Study I Protocols in comparison: ACE, NLR, ETX, ALARM, AODV I Performance evaluation: I I I I Streaming quality: Peak Signal-to-Noise Ratio Average number of “good streams” Packet drop ratio Average delay per packet ETX 1 0 2 − − AODV − ETX 2 − ALARM 0 − Number of video with good quality 4 NLR 1 7 X 7 Topology ACE 2 − − AODV − 3 ETX − ALARM 4 NLR − − Number of video with good quality 5 X 5 Topology ACE AODV − ALARM 3 NLR I ACE Number of video with good quality # of Good Streams Compared Video streams in good quality (PSNR ≥ 32 dB) 9 X 9 Topology 4 3 − − 1 − 0 45 40 35 30 25 25 25 20 20 AODV 45 ETX 45 ALARM 50 NLR 50 Average quality of video(dB) 7 X 7 Topology ACE AODV 30 ETX 35 ALARM 40 NLR 50 Average quality of video(dB) 5 X 5 Topology ACE AODV ALARM ETX NLR I ACE Average quality of Video(dB) Average Streaming Quality Average PSNR for all streams 9 X 9 Topology 40 35 30 Packet Drop Ratio 0.25 0.15 0.10 0.05 AODV ALARM ETX NLR 0.00 ACE Packet loss rate (%) 0.20 A Bigger Network - PSNR I 15 × 15 running IEEE 802.11g PSNR Scalability A Bigger Network - Packet Drop Ratio I 15 × 15 running IEEE 802.11g A Bigger Network - Delay I 15 × 15 running IEEE 802.11g Outline Introduction Video Streaming ACE: Modeling Simulations Admission Control Mobility Support ACE-AC: Principle I I Unlimited accommodation of calls will lead to a break-down. Our solution: I I I I λ µ Upon receiving a new request with advertised λ, estimate new utilisation ρ0 Threshold 1: ρG = 0.91 (‘Good’ quality, drop ratio Pd =0.1%) Threshold 2: ρA = 1 (‘Acceptable’ quality, drop ratio Pd =5%) Each node monitors its utilisation ρ = Admission vs. Utilisation 0 40 80 time(second) (A) 1.1 1.0 0.7 0.8 0.9 Utilisation ratio − (ρ) 1.0 0.7 0.8 0.9 Utilisation ratio − (ρ) 1.0 0.9 0.8 0.7 Utilisation ratio − (ρ) adding 6th video 1.1 adding 5th video 1.1 adding 4th video 0 40 80 time(second) (B) 0 40 80 time(second) (C) Admission Control - PSNR I 802.11g network of 15 × 15 topology I ACE-AC maintains high/good quality of existing streams while new streams are possibly added. Frame Quality Comparison Video frame at 43 sec., 4th stream. Clockwise: 5 streams; 6 streams with AC; 6 streams without AC. Outline Introduction Video Streaming ACE: Modeling Simulations Admission Control Mobility Support Introducing Route States p 1-p 1-q Connected Broken q Probabilities of ‘Connected’ and ‘Broken’ states: πc and πb . πc = πc (1 − p) + πb q, πb = πc p + πb (1 − q), πc + πb = 1. Solutions: q p+q p πb = p+q πc = A Simple Treatment According to Samar & Wicker (2004), when node speed v is low (< 5 m/s): I The link lifetime has an almost linear relationship to v . I The new link arrival rate remains stable. Hence we can assume p = kv , where k is a constant. πc = 1 q = , kv + q γv + 1 with γ = k/q. And the mobility-modified capacity is ∆m = ∆ 1 γv + 1 i.e. nodes with lower speeds are preferred. Mobility Simulation Setup I Test on impact of mobile population size, random speed (0.5,1) m/s: I I I I I Minority: 20% nodes moving Medium: 50% moving Majority: 80% moving 50 simulations run for each scenario with the same random initial topology Impact of speed: minority setting, nodes picking a random speed 0 - 5 m/s Performance Comparison 3.0 3.0 1.0 ACEm ACE NLR ETX ALARM AODV 2.0 Nf 1.5 2.5 1.5 0.5 1.0 0.5 0.0 Np 2.0 2.5 ACEm ACE NLR ETX ALARM AODV 1 2 3 4 5 Velocity (m/s) Number of videos in perfect quality 0.0 minority medium majority Motion scenarios Number of failed videos Ongoing / Future Work I Opportunistic routing in delay-tolerant networks (DTN) I Energy-efficient routing in MANET / DTN I Optimisation of wireless sensor networks with energy-harvesting