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An Intelligent Video Streaming Technique in ZigBee Wireless H. B. Kazemian Senior Member, IEEE
in physical systems using descriptive language, and thereby achieves tractability, robustness and inexpensive solution cost [2]. Research has been carried out into the application of fuzzy logic to many traffic control problems in wireless networks with noticeable degrees of success [3-4]. There have been many successful applications of fuzzy logic to MPEG video transmission over wireless networks. Sheu et al [5] proposed a fuzzy adaptive transmission rate control for frame transmission in wireless LANs. The fuzzy adaptive rate control considers the received signal strength indicator, the frame error rate and the medium access control delay to formulate a correct decision. Simulation results demonstrate that the proposed scheme improves the network throughput and the access delay. Furthermore, Kazemian and Meng [6] applied fuzzy control scheme to video transmission over IEEE 802.15.1 Bluetooth wireless. Computer simulation results demonstrate that in comparison with the open loop VBR system, the proposed control system significantly reduces both the variance of output bit rate to the network and the number of dropped data, which ultimately results to a guarantee in data source stability while maintaining image quality at the receiving end. Neural-Fuzzy also has been applied to audio transmission over wireless technology [7]. Su et al proposed a fuzzy neuron network scheme capable of adjusting input and output, increasing robustness, stability and working speed of the network for GPRS congestion control. As shown by the experimental results, the algorithm significantly prevents the congestion for video transmission, which proves that the method is more practical and effective than traditional methods for congestion control in wireless networks [8]. This paper takes the research further by applying a RBF technique and a NF technique to video transmission over ZigBee. It needs noting that ZigBee standard currently does not support video, and the originality of this work lies in the fact that the use of RBF and NF techniques enables video to be transmitted over ZigBee. Section II outlines an overview of the ZigBee protocol stack. Section III discusses the overall structure and the parameters of the RBF controller and the NF controller. Section IV describes the Matlab computer simulation results for a MPEG video clip. Section V concludes the overall results of the proposed RBF and NF controllers.
Abstract—This paper is concerned with an intelligent application of Moving Picture Expert Group (MPEG) video transmission over IEEE 802.15.4 – ZigBee. MPEG Variable Bit Rate (VBR) video is data hungry and presents excessive time delay and data loss over a wireless communication. Conventional rate policing such as generic cell rate algorithm is inadequate to sufficiently regulate transmission of VBR data sources over bandwidth limited ZigBee. Therefore, it is impossible to transmit MPEG VBR video over ZigBee channel. A buffer entitled ‘traffic-shaping buffer’ is introduced to prevent excessive overflow of MPEG video data over the ZigBee channel. A new Neural-Fuzzy (NF) scheme is developed to adjust the traffic-shaping buffer output rate to eliminate unacceptable delay or loss of the VBR encoded video and to conform the data to the token-bucket's contract prior entering the ZigBee channel. A Rule-Based Fuzzy (RBF) scheme is developed to monitor the data rate entering the traffic-shaper, in order to prevent either saturation or starvation of the buffer. The simulation results show that the use of the NF scheme and the RBF scheme enables MPEG VBR video to be transmitted over ZigBee.
I. INTRODUCTION
I
EEE 802.15.4 or ZigBee radio signal is low power, low cost, low complexity wireless standards. The maximum bandwidth supported by ZigBee wireless is very limited. Therefore because of the characteristics of ZigBee, there are many challenges to be overcome to allow low-congestion video to be transmitted over the wireless network. MPEG video compression utilizes similarities or redundancies between successive frames or images that exists in video signal, hence reduces the size of video stream [1]. MPEG uses motion vectors between frames to encode temporal redundancy and the discrete cosine transform to encode spatial redundancy. This is subsequently followed by runlength encoding. The result of this motion encoding scheme is that the Intra-frame (I-frame) is used to enable reconstruction of the following Bi-directional interpolatedframe (B-frame) or Predicted-frame (P-frame) until the next I-frame. Therefore, as past and sometimes future frames are used to create the next frame, this means that errors occur if any information is lost. The compressed MPEG video is irregular because of variation in bit rate and the Variable Bit Rate (VBR) coding in effect maintains the image quality. This paper utilizes a Neural-Fuzzy technique to reduce the burstiness of the MPEG VBR to transmit video over ZigBee. Fuzzy logic deals with imprecision and volatility inherent
II. IEEE 802.15.4 - ZIGBEE RADIO SIGNAL ZigBee has dual frequencies 868/915 MHz and 2.4GHz. The data rates for 868 MHz, 915 MHz and 2.4 GHz frequencies are 20 kbps, 40 kbps and 250 kbps respectively [9-10]. ZigBee is the only standards-based technology
H. B. Kazemian, London Metropolitan University, Faculty of Computing, Tower Building, Holloway Road, N7 8DB, UK (e-mail:
[email protected]). 978-1-4244-3597-5/09/$25.00 ©2009 IEEE
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designed to address the unique needs of low-cost, lowpower, wireless sensor networks for remote monitoring, home control, and building automation network applications in industrial and consumer markets. In this research, 2.4 GHz with the data rate of 250 kbps is utilized, as this frequency is used virtually worldwide. The 2.4GHz frequency band is part of the Industrial, Scientific and Medical (ISM) band and this band is used globally without the need to obtain a license. The 2.4 GHz ISM frequency is the same band as IEEE 802.11b, IEEE 802.11g, IEEE 802.11e, IEEE 802.15.1 and microwaves. ZigBee's technology is slower than 802.11b,g,e, but it consumes less power.
application support sub-layer (APS), the Application Framework (AF), the ZigBee device objects (ZDO) and the manufacturer-defined application objects (AOs). The APS sub-layer is responsible for binding that is the ability to match two devices together based on their services and requirements. The ZDO is responsible for describing the role of the device within the network (i.e., coordinator or end device), initiating and/or responding to binding requests, providing a secure relationship between network devices, discovering devices on the network and determining which application services they provide. The APS provides an interface between the Network Layer and the Application Layer through a general set of services that are utilized by both the ZDO and the manufacturer-defined AOs. The services are supported by two units are: the APS data unit (APSDU) through the APSDU service access point (APSDU-SAP) and the APS management unit (APSMU) via the APSMU service access point (APSMU-SAP). The APSDU facilitates the data transmission service for the transport of application Protocol Data Units between two or more devices located on the same network. Furthermore, the APSMU facilitates services for discovery and binding of devices and keeps a database of managed objects, known as the APS information base. In this paper MPEG video is transmitted through the APSDU. The data services provided by APSDU-SAP are request and response primitives for the data transfer. III. THE RBF AND NF SCHEMES FOR VIDEO TRANSMISSION OVER ZIGBEE Fig. 2 outlines the overall diagram of a Rule-Based Fuzzy (RBF) controller and a Neural-Fuzzy (NF) controller. A RBF controller supervises the output rate from the MPEG encoder and the arrival-rate at the traffic-shaper, in order to prevent either saturation or starvation of the buffer. The second control scheme is a NF controller, which observes and reduces the burstiness of the departure-rate from the trafficshaping buffer to enable the smooth transmission of MPEG VBR video over ZigBee satisfying two objectives, to reduce the traffic congestion in the network and to maintain the image quality. The inputs to the RBF controller are the fuzzified mean value X (k ) and the fuzzified standard
Fig. 1. IEEE 802.15.4 protocol layers compared to OSI.
In IEEE 802.15.4 - ZigBee, a data unit provides a data transmission service and a management unit provides all other services. Each service unit contacts with the upper layer through a service access point (SAP), and each SAP supports a number of service primitives to provide the necessary functionality. In Fig. 1, the ZigBee protocol stack architecture is based on the Open Systems Interconnection (OSI) seven-layer model. The IEEE 802.15.4 - 2003 standard defines the lower two layers: the Physical Layer and the medium access control (MAC) sub-layer. The ZigBee Alliance builds on these lower levels and introduces the Network Layer and the framework for the Application Layer [9]. The ZigBee Application Layer comprises of the
deviation (k ) of the queue length from the traffic-shaping buffer. The output from the RBF controller is the fuzzified time-interval of arrival data iˆa ( k 2) .
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R open(k 1) Rˆ a (k 2)
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Fig. 2. RBF and NF control for MPEG video transmission in ZigBee.
Rd ( f ) is between the arrival-rate Ra ( k 1) and the actual
The inputs to the NF controller are the fuzzified queue length X ( f ) from the traffic-shaping buffer and the fuzzified available tokens from the generic cell rate algorithm commonly known as token-bucket Y ( f ) . The
transmission rate ractual. The block ‘Desired-Arr-Rate’ calculates the desired arrival-rate Rˆ a (k 2) from the fuzzified time-interval of
output from the NF controller is the fuzzified departure data rate rd ( f ) . f is denoted for a frame. The fuzzified values
arrival data tˆa ( k 2) using equation (1):
of X (k ) , (k ) , iˆa (k 2) , X ( f ) , Y ( f ) and rd ( f ) are
Rˆ a (k 2) 1 /[tˆa ( k 2) (Ta _ max Ta _ min ) Ta _ min ]
kept within the range of [0 … 1]. The generic cell rate algorithm is based on a conventional policing mechanism known as token-bucket. Token-bucket is a rate policing buffer by which data streams can be compared to be complying with the wireless traffic contract [11]. The tokenbucket rate policing is inadequate to regulate transmission of MPEG VBR video data over 2.4 GHz ZigBee wireless. This paper presents another buffer called ‘traffic-shaping buffer’ before the departure-rate to manipulate and co-ordinate the VBR encoding video prior entering the ZigBee channel. The buffer’s role is to smooth the video output traffic and to partially eliminate the burstiness of the video stream entering the ZigBee network. The NF scheme manipulates I- and Pframes and if necessary drops B- frame, as loss of B- frame does not affect other frames in a Group Of Picture (GOP). Based on Phase Alternate Line (PAL), in the computer simulation it is assumed that there are 24 frames in one second and a GOP consists of 12 frames. Therefore there are two GOPs in one second. In Fig. 2, the k’th GOP is the picture group that has passed the network, the (k+1)’th GOP is the group which is in the process of passing through the model, and the (k+2)’th GOP is the group being encoded by the MPEG encoder. When the arrival-rate is greater than the departure-rate, data would be held in reserve in the tokenbucket; and when the memory space in the token-bucket is full, data would be dropped. The range of the departure-rate
(1)
Ta _ min and Ta _ max are defined in equation (2):
if ( Ropen (k 1) Rd ( f last )) Ta _ min 1 / 1.2 ractual Ta _ max 1.2 / ractual if (1/Ta _ max Ropen (k 1) 1 / Ta _ min )
(2)
Ta _ max 1 / Ropen (k 1) else Ta _ min 1 / Ropen (k 1) Ta _ max 1 / ractual The inputs to block ‘Desired-Arr-Rate’ also include, the departure-rate of the last frame in the k’th GOP R d ( f last ) , and the predicted data rate of the (k+1)’th GOP Ropen ( k 1) . In Fig. 2, the block ‘rd to Rd’ calculates the departure-rate R d ( f ) from the fuzzified departure-rate
rd ( f ) using equation (3).
Rd ( f ) rd ( f ) ( Rd _ max Rd _ min ) Rd _ min
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(3)
FUZZ-IEEE 2009, Korea, August 20-24, 2009
where
rd ( f )
is
the
fuzzified
Rd ( f ) ,
Rd _ min min{ Ra ( k 1), ractual }
8. If (Token-Y(f) is full) and (Queue-X(f) is medium) then (Output-rd is large) 9. If (Token-Y(f) is full) and (Queue-X(f) is full or very-full) then (Outputrd is very-large)
and (4)
and
Rd _ max max{ Ra ( k 1), ractual }
____________________________________________________
(5)
An adaptive technique is incorporated into the control scheme through the use of a NF controller. First, a neural network represents a RBFNF controller. A NF network conventionally has a three-layered architecture and uses fuzzy sets as its weights at the input and output layers. The nodes of the hidden layer provide the fuzzy IF-THEN rules. The standard back-propagation procedure for multi-layer neural networks is utilized to train the fuzzy membership functions. The parameters associated with the membership functions change through the learning process such that the network interprets the desired input/output map of the controller as accurately as possible. These parameters from the training procedure are fed back into the fuzzy system to produce the best control performance. In this paper, a backpropagation algorithm with a least squares method are used. The NF controller is based on Sugeno or Takagi-SugenoKang method [12-13]. The Sugeno method is ideal for acting as an interpolating supervisor of multiple linear controllers that are to be applied to different operating conditions of a dynamic nonlinear system. The Sugeno method works well with optimization and adaptive techniques. Therefore, the proposed adaptive scheme is a Sugeno RBFNF controller whose output membership functions are of a first-order. The general first-order Sugeno fuzzy model has rules of the form if x is A and y is B then z(x,y; ,,) = *x + *y +
Fig. 3. Da Vinci Code – Comparison of GOP size.
IV. COMPUTER SIMULATION RESULTS The NF controller is trained using the Matlab function ANFIS (Adaptive Neural-Fuzzy Inference System). This Matlab function performs adaptive Neural-Fuzzy training of Sugeno-type fuzzy inference. The proposed RBF and NF schemes have been tested on three real-time video clips, however, in this paper only one clip Da Vinci Code [15] is presented. A token-bucket size of 4 Kbytes is used in the Matlab computer simulation. The actual transmission rate (token rate) or bandwidth has been set as 200 kbps. With a token rate of 200 kbps, the token-bucket produces a maximum acceptable delay of 160 milliseconds (4*8/200 = 0.16 sec.). The frame rate in the experiments carried out is 25 frames/sec. Therefore, a maximum acceptable delay of 160 milliseconds amounts to the duration of 4 video frames. Quantization parameters are used in MPEG to determine and control the size and bit rate of the compressed video stream. The image quality is determined by the amount of compression specified by the quantization parameter of the encoder. Quantization parameters can be specified independently for each frame. In VBR the image quality is fixed, therefore the user specifies the quantization parameters, subsequently, the quantization parameters are fixed for all three I-, P-, and B-frames in this work.
(6)
where A and B are the antecedents, while , , and are all constants. The RBFNF controller [14] is incorporated in the NF controller. Table 1 represents the initial 9 rules associated with the RBFNF controller. The output of RBFNF controller is the fuzzified departure-rate
rd from the traffic-shaper.
TABLE I RULES OF THE RBFNF CONTROLLER _______________________________________________ 1. If (Token-Y(f) is empty) and (Queue-X(f) is empty or medium or full) then (Output-rd is very-small) 2. If (Token-Y(f) is empty) and (Queue-X(f) is very-full) then (Output-rd is small) 3. If (Token-Y(f) is medium) and (Queue-X(f) is empty) then (Output-rd is very-small) 4. If (Token-Y(f) is medium) and (Queue-X(f) is medium) then (Output-rd is intermediate) 5. If (Token-Y(f) is medium) and (Queue-X(f) is full) then (Output-rd is large) 6. If (Token-Y(f) is medium) and (Queue-X(f) is very-full) then (Output-rd is very-large) 7. If (Token-Y(f) is full) and (Queue-X(f) is empty) then (Output-rd is verysmall) 124
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dropped data for the RBF and NF schemes, and it is calculated by adding values from both buffers, traffic-shaper and token-bucket, and re-plotting as if the system has only one combined buffer. Table 2 is a supplement to Figures 3, 5 and 6. It provides summarized values for both plots demonstrating that the standard deviation of the RBF and NF systems is lower than the standard deviation obtained for the conventional VBR system, hence less burstiness. In Table 2, the GOP size is averaged for both systems and there are less GOP sizes for the RBF and NF systems than the conventional VBR system, which reduces data loss but still maintains picture quality. Furthermore in Table 2, there is no traffic-shaping buffer for the conventional VBR system and the overall dropped data through the token-bucket is 2.711%. With the RBF and NF systems, there is no data loss at tokenbucket and the total loss is at the traffic-shaping buffer, which is 0.448%. The advantage of the RBF and NF system is that the data loss at the traffic shaper can be monitored and manipulated, hence minimizing the picture quality degradation. From the table, it can consequently be concluded that the percentage of the data loss using the proposed RBF and NF system is much less than the conventional VBR system. As stated in section I, the B picture is allowed to be dropped in this paper. The motion vector is a very important part in reconstructing the image for the B picture. With full set or part of the motion vector, the image can be rebuilt from the reference frame(s) [1].
Traffic Shaper Queue Length, X(f)
Fig. 4. Da Vinci Code – Results for traffic-shaper arrival-rate (Ra), trafficshaper departure-rate (Rd), and actual transmission rate (ractual).
Token Bucket Available Space, Y(f)
Fig. 3, ‘Da Vinci Code’ provides the comparison of compressed video stream with and without the RBF and NF schemes. Fig. 3 demonstrates less concentration of GOP size in one area for the RBF and NF schemes, more distribution and less burstiness, which provides reduction in data loss and time delay and hence facilitates video transmission over ZigBee. Furthermore, Fig. 4 shows the proposed RBF and NF schemes with Ra, Rd and ractual. As shown, the proposed schemes manipulate the MPEG encoder and regulate the bit rate in order to decrease the burstiness in Rd resulting in reduction in data loss and delay, and enabling video transmission over ZigBee. Fig. 5 shows separate plots for each buffer, the conventional token-bucket and the trafficshaper from the proposed design. The token-bucket buffer availability below 0 and the traffic-shaper queue length above 1 mean the system is experiencing data drop. The token-bucket buffer works as such that the more token is taken the less space will be available to store the video data, hence resulting into data loss. The traffic-shaping buffer works as such that the more video data is deposited, the less buffer will be available, resulting into values moving from 0 to 1. The data loss in Fig. 5 is at the traffic-shaping buffer, which the degree of loss could be monitored by constantly checking the capacities of the two buffers and the network in real-time and providing a scenario where the video data is mainly lost at the traffic-shaping buffer. There is no loss at the token-bucket buffer. Fig. 6 is the compatible plot for the
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Fig. 5. Da Vinci Code – Result for traffic-shaper queue length and tokenbucket available space.
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reduction in data loss and time delay, and hence enables video transmission over ZigBee. As the RBF and SOF schemes are based on a simple rule-base and a neural-fuzzy, the proposed system does not require a great processing power which could be useful for time-sensitive MPEG VBR video services over wireless technology.
ACKNOWLEDGMENT This work was funded by the Emerald Grant UK. The work was carried out at London Metropolitan University, UK.
Fig. 6. Da Vinci Code – Plot for total buffer availability.
REFERENCES [1]
TABLE II DA VINCI CODE – SUMMARY OF RESULTS FOR RBF & NF AGAINST CONVENTIONAL VBR SYSTEM
[2]
[3]
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RBF & NF control system
Standard deviation of GOP size (kbit)
63.95
59.63
Average GOP size (kbit)
458.18
423.37
% of dropped data at token-bucket
2.711
0
[7]
% of dropped data at traffic-shaper
N/A
0.448
[8]
Total % of dropped data
2.711
0.448
[4]
[5]
[6]
[9]
[10]
V. CONCLUSION
[11]
IEEE 802.15.4 ZigBee is a standard designed to address the needs of low-cost, low-power, wireless sensor networks for remote monitoring, home control, and building automation network applications in industrial and domestic market. This paper starts experimenting with transmitting a new medium such as video over ZigBee. The overall objective of this paper is to reduce the oversubscribed MPEG VBR traffic entering the ZigBee channel to enable video to be transmitted over the network. For this the novel RBF and NF controllers are introduced to police and adjust the traffic-shaping buffer departure-rate. The computer simulation results demonstrate less concentration of GOP size in one area, more distribution and less burstiness using the RBF and NF schemes. This combination provides
[12]
[13] [14]
[15]
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