Media-Aware Multi-User Rate Allocation over Wireless Mesh Network

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Media-Aware Multi-User Rate Allocation over Wireless Mesh Network Xiaoqing Zhu and Bernd Girod Information Systems Laboratory, Stanford University, CA, U.S.A. {zhuxq,bgirod}@stanford.edu

Abstract— When multiple video streams are transmitted in a wireless mesh network, each stream needs to adapt its rate to the time-varying traffic in the network. We propose a media-aware rate allocation algorithm that adjusts the video rate based on both video content and network congestion. This is combined with cross-layer information exchange: the video agent relies on estimated link state at each hop along the path, as well as accumulated congestion increment reported by the routing agent. We discuss in detail how the distributed optimization can be realized at each wireless node, and present simulations of the proposed rate allocation algorithm over an 802.11 wireless mesh network. Experimental results confirm that the proposed scheme can effectively adapt the allocated rate to the presence of new streams in the network in a congestion-distortion optimized manner. In comparison with TCP-Friendly Rate Control (TFRC), the proposed scheme can achieve a higher average video quality for all users while maintaining lower overall network congestion.

I. I NTRODUCTION A collection of wireless nodes can self-organize into a wireless mesh network. Each node can serve as a sender, a receiver, or a relay. Such a system can be deployed with low cost and high flexibility. Support of media streaming sessions over such a network is compelling for many applications, ranging from audiovisual communication in search-and-rescue operations, multi-camera wireless surveillance networks, to media streaming over wireless home networks or extended service area for broadband Internet access [1]. More recently, the potential of mesh networking among cellphone devices has also been explored for reducing the service cost of providing 3G multimedia contents to multiple users [2]. Realization of these applications in practice is still hindered by many technical challenges. Wireless channels may exhibit fluctuating link qualities caused by interference, multi-path fading and shadowing. The traffic patterns of compressed media streams change over time due to content variations and dynamic user behavior. Streaming applications usually have high data rate and stringent latency requirements, at odds with the limited bandwidth resources in a wireless network. Moreover, simultaneous streaming of multiple video sessions can easily lead to network congestion without careful rate allocation. As the video contents differ for each stream, the utility of the allocated rates is also different; the same increase in rate may have a greater impact on one stream than on another. We This work is partially supported by NSF Grant CCR-0325639.

therefore propose to perform multi-user rate allocation in a media-aware fashion, so as to maximize the total utility across all users in the network. The optimal allocated rate for each stream also needs to adapt to the time-varying wireless channel conditions and network congestion. In our work, this is achieved via crosslayer information exchange. Wireless link states are monitored at the MAC layer by logging packet arrival and departure events. Impact of the allocated rate on network congestion is collected along the path from source to destination by the routing agent at the network layer. All this information is passed along to the streaming agent at the application layer to determine the optimal video encoding rate. In addition, the proposed scheme performs both routing and rate allocation in a distributed manner so that computational burden can be shared among all nodes in the network. This avoids the traffic overhead introduced by collecting global network information in a centralized scheme. As each node performs the optimization based on local observations, the distributed scheme is also more responsive to variations in network conditions. Due to the CSMA/CA mechanism of 802.11 [3], contention among traffic over neighboring links will cause fluctuation in the observed effective link capacity, and in turn will affect the allocated rate. Since it is difficult to capture such interaction analytically in the network model, we study its impact on the behavior of our proposed scheme via network simulations. The rest of the paper is organized as follows. After a brief survey of related work in Section II, the overall architecture of the proposed cross-layer media-aware rate allocation scheme is presented in Section III. We then explain each component in our system across the protocol stacks: link state estimation in Section IV, congestion-minimized routing in Section V and congestion-distortion optimized rate allocation in Section VI. Simulation results for multi-user video streaming over wireless 802.11 mesh network are discussed in Section VII. II. R ELATED W ORK Rate allocation among multiple traffic streams over a common network is a well-studied problem. The mathematical formulation of the problem, as well as two classes of distributed rate control algorithms corresponding to the primal and dual decomposition of the optimization are explained in [4] and [5]. Application of such rate allocation algorithms has been investigated for elastic traffic over the Internet [6]. In a more

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practical setting, a rate allocation algorithm combined with a packet partitioning algorithm are presented in [7] to support video streaming from multiple senders to a single receiver over the Internet. For video streaming over wireless networks, the problem of rate adaptation over a single link with fluctuating effective bandwidth has been formulated in the framework of stochastic dynamic programming, in [8] for rate control of live-encoded video and in [9] for pruning pre-encoded video packets. In the multi-user scenario, centralized cross-layer optimization of air time allocation among multiple wireless stations has also been studied [10]. In [11], rate allocation among multiple potential media servers is jointly optimized with route selection for delivering a requested video stream over the wireless mesh network. In our own work, we have proposed distributed optimization algorithms for route selection and rate allocation, both for a simple network model assuming fixed link capacities [12][13], and for wireless mesh network composed of 802.11 nodes [14]. III. S YSTEM OVERVIEW Consider simultaneously supporting multiple video streams over a common wireless mesh network. Each node consists of a link state monitor at the MAC layer, a congestion-minimized routing agent at the network layer, and a video streaming agent at the application layer. Traffic from different video sessions over neighboring links contend over the common wireless media for transmission opportunities. Effective bandwidth and existing traffic rate is estimated on-the-fly by the link state monitor. Source routing is used to reduce overhead of maintaining routing tables. Each video session may travel over multiple hops from source to destination along a path specified by the routing agent. The optimal path selection is carried out in a distributed manner among all potential relay nodes in the network. Rate allocation to each video stream is also performed in a decentralized fashion. Each source node determines the optimal rate for its video session to balance the competing objectives of improving encoded video quality and limiting incurred network congestion. Figure 1 illustrates various components in such a system. Figure 2 depicts the cross-layer information exchange among the agents. At Node n, the link state monitor collects effective link capacity Cn and existing traffic flow Fn . Such information is used by the routing agent to choose a relay node incurring minimum congestion increment ∆Xn (Cn , Fn ) s over P the next hop. End-to-end congestion increment ∆X = n∈P s ∆Xn for Stream s is accumulated along the selected path P s by the routing agent, and fed back to the sender. Given the distortion-rate tradeoff D s (Rs ) for encoding its own video sequence, the streaming agent finds the optimal allocated rate Rs,opt to achieve a balance between decreasing encoded video distortion ∆D s and increasing network congestion ∆X s reported from the routing agent.

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System of multi-user video streaming over wireless mesh network.

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Fig. 2. Cross-layer information exchange among the link state monitor, the routing agent and the application layer video streaming agent in a system with multi-user video streaming over wireless mesh network.

each link by logging packet arrivals and departures at the MAC layer, assuming that the 802.11 protocol is used for wireless media access [3]. A. Estimation of capacity and flow For a given period of time Ttotal on each node, we denote Tbusy as the total time that the node spends for transmitting the packets, including MAC layer overhead such as RTS/CTS/ACK packets. Tblock records the average time during which the node is blocked from transmission either due to presence of other transmissions or due to the backoff procedure in the carrier sense and collision avoidance (CSMA/CA) mechanism. Tidle refers to the total time for which the node remains idle and is ready for transmitting the next packet: (1)

IV. L INK S TATE E STIMATION

Ttotal = Tbusy + Tblock + Tidle .

In this Section, we describe how the link state monitor estimates the effective capacity and existing traffic rate over

Figure 3 illustrates the three different time periods observed by one node in the network over multiple cycles of packet

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Fig. 3. Illustration of how to categorize observed channel state into T busy , Tblock and Tidle on a wireless node following the 802.11 MAC protocol.

transmission. By keeping a running average of Tbusy , Tblock and Tidle , the flow rate for Stream s on Node n can be estimated as: Fns =

Bs Bs = , Ttotal Tbusy + Tblock + Tidle

(2)

where B s is the average video payload size from StreamPs over the period. Total traffic rate over the node is Fn = s Fns , and estimated bandwidth is1 : P s sB , (3) Cn = Tbusy + Tblock which takes into account packets from all streams. As the effect of instantaneous bit-error-rates or collisions are captured by Ttotal and the relative proportions of Tbusy , Tblock and Tidle , our estimation of effective bandwidth Cn is automatically updated according to the variations in wireless channel conditions.

B. Estimation of available bandwidth For Stream s over Node n, the maximum supportable rate is given by: X 0 Fns , (4) Cns = Cn − s0 6=s

where Fns ’s denote existing traffic rates from all other streams on that node. The end-to-end available bandwidth corresponds to the bottleneck value along the path P s from source to destination: 0

s Cavail = mins Cns , n∈P

(5)

and serves as an upper bound for the allocated rate for each stream. 1 Due to the broadcast nature of the wireless medium, and the fact that traffic for different destinations share the same queue on a wireless terminal, we estimate the capacity for each node, instead of for each link, as the average service rate to the queue.

C. Estimation of Link Congestion Congestion is defined as proportional to the average delay experienced by packets traveling over that link. Assuming the M/M/1 queuing model for packet arrivals and departures2, congestion over Node n can be estimated as [15]: 1 Xn = . (6) Cn − F n Consequently, the congestion increment caused by supporting an additional amount of traffic ∆R over the same link can be derived as: 1 ∆Rn . (7) ∆Xn ≈ (Cn − Fn )2 which increases nonlinearly as the traffic load approaches the effective link capacity. V. C ONGESTION -M INIMIZED ROUTING The goal of congestion-minimized routing is to find one or multiple paths from source to destination so as to minimize the increase in network congestion introduced by the new video stream. Detailed descriptions of the scheme can be found in [12] and [13]. For the completeness of this paper, a brief sketch of the routing algorithm is included in this section. Consider a network with N nodes, with observed effective capacity Cn and flow Fn on each node. Overall network congestion can be calculated as: X=

N X

Fn . C − Fn n n=1

(8)

For Stream s, the congestion increment introduced by a small rate increment ∆Rs is shown to be: X Cn ∆Rs , (9) ∆X s ≈ 2 (C n − Fn ) s n∈P

along a chosen path P s . Therefore, the optimal path can be achieved by minimum-cost P routing, where the total cost from source to destination n∈P s Cn /(Cn − Fn )2 is independent of the amount of traffic to be routed ∆Rs . In fact, the link cost Cn /(Cn − Fn )2 , as the derivative of the function F/(C − F ) with respect to F , can be interpreted as the the sensitivity of total network congestion to additional traffic rate on that link. This approximation holds when ∆Rs is small. Unlike in a network with fixed link capacities, distribution of traffic over multiple paths composed of 802.11 wireless nodes will not reduce network congestion. Traffic from neighboring links contend for common wireless resource, resulting in lower effective channel capacity at each link. Therefore, in this work, only a single path is selected for each video stream. s As the allocated rate to the video stream increases by ∆R(k) at each step, the congestion increment need to be updated along the same path P s : X Cn s ∆X(k) ≈ ∆Rs(k) , (10) Pk−1 s )2 (C − F − ∆R 0 s 0 n n k =1 n∈P (k )

2 Even though actual video traffic patterns do no necessarily behave like M/M/1 queues, this assumption captures the nonlinear relationship between traffic load and capacity. It also allows analytical prediction of the congestion increment ∆X introduced by traffic rate increment ∆R.

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three or more trial encodings using nonlinear regression techniques. The distortion reduction caused by increasing encoding rate by ∆Rs is therefore: −∆Ds ≈

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Fig. 5. Video distortion reduction and network congestion increment (scaled by λ = 50) introduced by increasing allocated rate of each video stream in the wireless mesh network simulated in Subsection VII-C.

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(b) Fig. 4. Initial (a) and final (b) routes selected by the proposed distributed congestion-minimized routing algorithm, when the third video stream Mother and Daughter enters the wireless mesh network simulated in Subsection VIIC. Routes for the Bus sequence are plotted in blue dotted lines. Red solid lines represent Foreman and pink dashed lines are used for Mother and Daughter. s where ∆R(k 0 ) ’s are the previously allocated rates for that stream. In practice, the protocol can be implemented by modifying the link cost measure of some minimum-hop-based ad-hoc routing protocols such as Dynamic Source Routing (DSR) [16]. As an illustration, Fig. 4 shows the initial and final routes when a third video stream enters the network in the simulation scenario described in Subsection VII-C. Decrease in encoded video distortion and increase in network congestion reported by the distributed routing agent are plotted in Fig. 5.

VI. M EDIA -AWARE R ATE A LLOCATION At the application layer, the video stream s is associated with a mean squared error (MSE) decoding distortion of D s when encoded at rate Rs . The distortion-rate (DR) characteristic of each stream can be fitted to a parametric model [17]: θs , (11) Ds (Rs ) = D0s + s (R − R0s )

where the parameters D0s , θs and R0s depend on the coding scheme and the video content. They can be estimated from

(Rs

θs ∆Rs . − R0s )2

(12)

The objective of the rate allocation algorithm is to minimize the encoded video distortion of all users, while limiting the increase in network congestion. In [13], this is formulated into a convex optimization problem, the objective function being the Lagrangian cost of both terms: min

Rs ,P s ,s=1,...,S

S X

Ds (Rs ) + λX,

(13)

s=1

and the linear constraints being flow conservation of each stream. The readers are referred to the original paper for a discussion of both centralized and distributed solutions for the joint optimization of routing and rate allocation. In this work, the procedures for the distributed optimization of rate allocation are adopted, for video streaming over 802.11 wireless mesh network. At each time step k, the source node increases the allocated s rate to the Stream s by ∆R(k) , and compares the congestion s increment ∆X(k) reported by the routing agent as in Eq. (9), s versus the video distortion reduction −∆D(k) as in Eq. (12). s s The allocated rate can increase by ∆R(k) unless −∆D(k) < s λ∆X(k) , i.e., when the benefit of distortion reduction is no longer worthwhile the increase in network congestion. Due to the convex nature of both D s and X, the initial distortion reductions are typically significant for small rate increments, whereas increase in network congestion starts out slowly. Therefore, the rate allocation algorithm can continue until it reaches the optimal rate that strikes a balance between the two tradeoff slopes. When multiple users are present in the network, they need to agree upon a common tradeoff factor λ, and take turns to optimize its own rate allocation, treating traffic rate from all other video streams as background. It is shown in [13] that, for a network with fixed link capacities and flow rates,

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since the overall Lagrangian cost in Eq. (13) keeps decreasing after each user’s optimization, the distributed algorithm is guaranteed to converge. Over an 802.11 wireless network, however, due to the underlying fluctuation of link capacity and flow rates, the optimal path chosen by the routing agent may oscillate among several alternatives, and the observed congestion increment over a given path may also fluctuate over time. Both phenomena lead to variations in the allocated video rates, as observed in our simulative study.

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B. Multiple streams over single-hop connections In this section, we consider the simple scenario of multi-user video streaming over single-hop connections. The Foreman sequence is streamed over a single hop from Node 7 to Node 1 for the first 600 seconds, while the Mother and Daughter sequence from Node 6 to Node 4 becomes active 300 seconds later, and lasts for another 600 seconds. Note that even though the two video streams do not share any nodes for transmission, their traffic contend for the common wireless media via the 802.11 MAC protocol. This can be observed in Fig. 8. The Foreman sequence automatically adapts to the presence of the

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Fig. 6. Wireless mesh network with 10 nodes randomly positioned in an 100m-by-100m square area. The wireless nodes are simulated in ns-2 following the 802.11 MAC protocol. Transmission range of each node is 60m and multi-hop delivery is needs for source-destination pairs outside of this range.

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To verify the effectiveness of the proposed distributed rate allocation scheme, experiments are carried out using ns-2 [18] for a simulated network consisting of 10 static nodes randomly positioned in an 100m-by-100m square area. Fig. 6 plots the positions of the wireless nodes. Receiver sensitivity parameters are adjusted so that the communication range of each node is 60m. Multi-hop transmission is therefore needed, and the RTS/CTS handshake mechanism in the 802.11 protocol is invoked to avoid the hidden terminal problem. Each link operates at a fixed data rate of 5.5 Mbps for payload and 1.0 Mbps for MAC-layer control packets. Three CIF video sequences of decreasing content complexity: Bus, Foreman and Mother and Daughter are considered for streaming between various source-destination pairs. The tradeoff between encoding rate and video quality is obtained by empirically encoding each stream using the H.264/AVC codec [19] at various quantization step sizes, with a frame rate of 30 fps and GOP length of 15. The experimental data points are then fitted with the parametric model in Eq. (11), as plotted in Fig. 7. In the following, we first study the dynamic behavior of the proposed algorithm in the simple scenario of two contending video sessions over neighboring wireless links in Subsection VII-B. The more general case of three video streams each traveling over a multi-hop path is investigated in Subsection VII-C. Finally, performance of our proposed media-aware rate allocation scheme is compared with the conventional media-unaware approach, where the rate of each video stream is determined by the TCP-Friendly Rate Control (TFRC) agent at the transport layer [20]. Most experiments are carried out by sweeping the congestion-distortion tradeoff factor λ from 5 to 500. Re-optimization of routing and rate allocation is performed once every 4 seconds.

35 Bus, model Bus, experimental Foreman, model Foreman, experimental Mother and Daughter, model Mother and Daughter, experimental

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Fig. 7. Rate-PSNR performance of Bus, Foreman and Mother and Daughter CIF video sequences, all encoded using the H.264/AVC codec at 30 frames per second, with GOP length of 15. The experimental data points are fitted with the model curves using nonlinear regression techniques.

second stream Mother and Daughter by gradually reducing its allocated rate. In this example, it is also interesting to note that unlike in a network with fixed capacity, the available bandwidth Cavail estimated by the link state monitor at each 802.11 node changes with variations in the traffic load. When both video sequences are active, the estimated Cavail of the first link for Foreman is indeed reduced due to contentions from traffic of the Mother and Daughter sequence. C. Multiple streams over multi-hop paths We now consider the more general case of supporting multiple video streams each over a multi-hop path, dynamically optimized by the congestion-minimized routing agent. The simulation lasts for 900 seconds, with the Bus (from Node 3 to Node 5), Foreman (from Node 2 to Node 9) and Mother and Daughter (from Node 8 to Node 10) sequences becoming active at the 5th, 210th and 410th second, respectively, each

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lasting for about 600 seconds. The simulation events are illustrated in Fig. 9. By this design, one can examine rate allocation results for various combinations of users sharing the network. Figure 10 shows the trace of allocated rate for each sequence over time, given an intermediate tradeoff factor of λ = 50. As new streams join the network, the allocated rate of the existing streams are reduced gradually. During the period between 410s and 610s, when all three streams are present, their allocated rate reflect their relative complexity, i.e., the most complex stream Bus is allocated the highest rate while the sequence with the least motion Mother and Daughter is assigned the lowest rate. One can also observe the fluctuations in the allocated rates, due to reasons mentioned in Section VI. In Fig. 11, the average and standard deviation of the allocated rate for each stream is plotted by varying the tradeoff factor λ from 5 to 500. When λ is small, the allocated rate to each stream is mainly limited by the end-to-end available bandwidth over the path. As λ increases, the constraint on network congestion becomes increasingly dominant, leading each user to backoff its video rate. The amount of fluctuation in rate allocation also decreases with a greater λ, as capacity and flow estimation tend to have more stable results over less congested links.

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Fig. 10. Allocated rate for Bus (blue dotted line), Foreman (red diamond markers) and Mother and Daughter black plus signs given a common tradeoff factor λ = 50 during the events depicted in Fig. 9.

D. Comparison with TFRC In this section, we compare the performance of our proposed scheme to the conventional media-unaware approach, where the encoding rate of each video stream is determined by the

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Fig. 8. Estimated effective bandwidth and allocated rate for the Foreman and Mother and Daughter sequence, each traveling over a single-hop connection. In this experiment, λ = 50.

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Fig. 9. Scheduled events for the simulation of multi-user video streaming over multi-hop paths. The Bus stream travels from Node 3 to Node 5, and is active from time 5.0s to 610.0s. Foreman travels from Node 2 to Node 9, and is active from 210.0s to 820.0s. The Mother and Daughter session is between Node 8 and Node 10, starting at 410.0s and finishing 1020.0s.

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(b) Fig. 11. Average and standard deviation of allocated rates for Bus, Foreman, and Mother and Daughter sequences over the a common wireless mesh network. The congestion-distortion tradeoff factor λ ranges from 5 to 500.

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TCP-Friendly Rate Control (TFRC) equation: R=

kS √ . RT T p

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Average

(14)

where S, RT T, p and k denote the packet size, the round-triptime estimate, the loss rate estimate and a scaling constant respectively [20]. Figures 12, 13 and 14 compare the allocated rate, corresponding encoded video quality and end-to-end delay between the proposed media-aware scheme and TFRC. For the proposed scheme, the intermediate value of 50 is chosen for λ, leading to similar end-to-end delay performance as achieved by TFRC. Comparisons of the average rate, video quality3 and delay are also included in the last column of the graphs. As shown in Fig. 12, the proposed media-aware scheme tends to allocate more rate to the Bus sequence with more dynamic content, which needs to be encoded with greater rate to achieve a basic quality. The TFRC scheme, on the other hand, is unaware of the relative content complexity of the sequences, and somehow resulted in the reverse order of allocated rates among the sequences. Consequently, the proposed media-aware approach can achieve a higher average video quality while maintaining lower overall network congestion, as measured by the weighted average end-to-end delay of all three streams in Fig. 14. VIII. C ONCLUSIONS In this work, we study the behavior of a media-aware distributed rate allocation scheme for multi-user video streaming over wireless mesh network. By taking advantage of cross-layer information exchange from the link state monitor and the congestion-minimized routing agent, the streaming agent at each user can adapt its video rate to changes in the network in a congestion-distortion optimized manner. Simulation results involving multiple video streams over a simulated mesh network with 802.11 wireless nodes confirm the effectiveness of the proposed scheme. Compared with the 3 Average video quality among all users is measured as the PSNR corresponding to the average MSE distortion of all encoded video sequences.

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Fig. 13. PSNR in dB corresponding to the average encoded distortion of the video sequences Bus, Foreman and Mother and Daughter resulting from rate allocation using the proposed media-aware approach (λ = 50) versus conventional TFRC. PSNR values in the last column correspond to the average MSE encoded distortion of all three streams.

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Fig. 14. Average end-to-end delay experienced by each video stream resulting from rate allocation using the proposed media-aware approach (λ = 50) versus conventional TFRC. The last column compares the average delay over the entire network, each user weighted by its rate. This measurement corresponds to the overall congestion in a network.

conventional media-unaware approach such as TCP-Friendly Rate Control, the proposed scheme is shown to achieve higher average encoded video quality while maintaining lower overall network congestion. For future work, we intend to improve the convergence speed of the rate allocation procedure, and to investigate performance of the scheme in a network containing mobile nodes. R EFERENCES [1] S. Cass, “Viva mesh vegas [mesh wireless network],” IEEE Spectrum, vol. 42, no. 1, pp. 48–53, Jan. 2005. [2] S.-H. Chan M.-F. Leung and O. Au, “Cosmos: Peer-to-peer collaborative streaming among mobiles,” Proc. IEEE International Conference on Multimedia Expo (ICME’06), Toronto, Canada, July 2006. [3] IEEE Standard for Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, P802.11, Nov. 1997. [4] F. P. Kelly, “Charging and rate control for elastic traffic,” European Trans. on Telecommunications, vol. 8, no. 1, pp. 33–37, Jan. 1997.

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