On Energy Efficient Encryption for Video Streaming in Wireless Sensor ...

IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 12, NO. 5, AUGUST 2010

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On Energy Efficient Encryption for Video Streaming in Wireless Sensor Networks Wei Wang, Member, IEEE, Michael Hempel, Member, IEEE, Dongming Peng, Member, IEEE, Honggang Wang, Member, IEEE, Hamid Sharif, Senior Member, IEEE, and Hsiao-Hwa Chen, Fellow, IEEE

Abstract—Selective encryption for video streaming was proposed for efficient multimedia content protection. However, the issues on joint optimization of video quality, content protection, and communication energy efficiency in a wireless sensor network (WSN) have not been fully addressed in the literature. In this paper, we propose a scheme to optimize the energy, distortion, and encryption performance of video streaming in WSNs. The contribution of this work is twofold. First, a channel-aware selective encryption approach is proposed to minimize the extra encryption dependency overhead at the application layer. Second, an unequal error protection (UEP)-based network resource allocation scheme is proposed to improve the communication efficiency at the lower layers. Simulation experiments demonstrate that the proposed joint selective encryption and resource allocation scheme can improve the video transmission quality significantly with guaranteed content protection and energy efficiency. Index Terms—Cross layer approach, resource allocation, video selective encryption, wireless sensor network.

I. INTRODUCTION

V

IDEO streaming protocols such as MPEG-4 H.264/AVC [1]–[3] have gained popularity for their wide applications in wireless sensor networks (WSNs). It is well known that many applications in WSNs are mission critical, such as battlefield assistance, adversary intrusion detection, distributed signal processing, etc. In these applications, the multimedia semantic information and video content are extremely sensitive to malicious attacks, including eavesdropping/intercepting of ongoing traffic, and manipulating/counterfeiting of the existing media flows. In this paper, we tackle the challenges of eavesdropping and intercepting attacks in WSNs, motivated by a simple but critical fact, i.e., the accessibility of surveillance video stream

Manuscript received May 23, 2009; revised February 09, 2010; accepted April 01, 2010. Date of publication May 18, 2010; date of current version July 16, 2010. This work was supported in part by US NSF Grant No. 0707944 for wireless sensor networks research, and in part by Taiwan National Science Council Research Grant NSC98-2219-E-006-011. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Ali N. Akansu. W. Wang is with the Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD 57006 USA (e-mail: [email protected]). M. Hempel, D. Peng, and H. Sharif are with the Department of Computer and Electronics Engineering, University of Nebraska-Lincoln, Lincoln, NE 68508 USA (e-mail: [email protected]; [email protected]; [email protected]). H. Wang is with the Department of Electrical and Computer Engineering, University of Massachusetts-Dartmouth, North Dartmouth, MA 02747 USA (e-mail: [email protected]). H.-H. Chen is with the Department of Engineering Science, National Cheng Kung University, Tainan 70101, Taiwan (e-mail: [email protected]). Digital Object Identifier 10.1109/TMM.2010.2050653

content information to adversaries will reveal the location of cameras to them, jeopardizing information retrieval functionalities critical to the WSNs. Conventional data encryption schemes such as advanced encryption standard (AES) and elliptic curve cryptography (ECC) are difficult to be applied directly to wireless video privacy protection due to the large volume of data and real-time transmission requirements. Selective encryption [4], [5] has been proposed recently as an ideal candidate to achieve content protection. However, most of the previous works on multimedia selective encryption have been focused mainly on the application layer cryptography, and they did not consider the optimal delivery of encrypted video frames in error-prone wireless channels. Furthermore, the study on critical energy-distortion optimization in WSNs with flexible network resource allocation is missing in the literature. The works in [4] and [5] surveyed state-of-the-art multimedia privacy protection technologies, and the authors strongly advocated selective encryption as an efficient solution to achieve multimedia secrecy. Lian et al. [6] proposed a partial encryption scheme aiming to hide important MPEG-4 H.264 content information. In an approach revealed in [6], the intra-prediction mode information, residue data, and motion vectors were selected and ciphered based on the Exp-Golomb coding, the context-adaptive variable length coding (CAVLC), and the sign coding. The work reported in [7] also proposed a selective encryption algorithm for H.264/AVC video, in which the first eight bytes of data in each macro block were encrypted based on data encryption standard (DES) to distort the video visual quality. The works demonstrated in [7] and [8] proposed the schemes for the frequency domain scrambling of the significant transformation coefficients to avoid video content fruition by unauthorized or illegitimate users. The carefully designed scrambling techniques in those approaches considerably alleviates the plaintext attacks. Efficient quad-tree and zero-tree-based selective encryption approaches were suggested in [9] for digital images and videos. In [9], ciphering the tree structure information in two highest pyramid levels critical to the whole decoding process has been proved to be very effective to protect the secret multimedia data. Some similar researches regarding video/multimedia selective encryption have been reported in the literature [10]–[12], where they ciphered a selected set of discrete cosine transform (DCT), wavelet coefficients, or bit-planes to reduce encryption bit rate overhead. The other related works regarding selective encryption can be traced back to the late 1990s such as [24] and [25]. The work in [13] proposed several different approaches to achieve multimedia privacy by combining multimedia encryption and entropy coding to a single

1520-9210/$26.00 © 2010 IEEE

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process. They proposed to use multiple Huffman tables (MHT) alternately in a secret order, which was created using a pseudorandom number generator. According to their study, a reasonably higher level of security and lossless media compression efficiency have been achieved. Similar works regarding MHTbased multimedia cryptography were reported in [14] and [15]. More recently, the research work shown in [16] suggested that the cryptographic strength in terms of key length may make the proposed MHT approaches very sensitive to channel errors, and the provision of multimedia security would result in severe throughput loss in a wireless channel if those approaches were used. In all the aforementioned works, various challenging issues of improving energy efficiency in WSNs and a joint design of energy-distortion-encryption schemes were still not considered. In our previous work [19], we analyzed digital image coding dependency and proposed a new UEP scheme by considering image position and value diversity. The main idea given in that work was considered in image encryption [17], [18] and image authentication as presented in [20]. However, our previous works were focused on providing security for a single image only, without considering the temporal correlation and dependency among multiple pictures in a video sequence. The motion estimation/compensation and the residue coding from the inter-frame video compression have a great potential to further improve the efficiency of encryption and network resource allocation in WSNs. Different from most of the aforementioned works, this paper proposes a new WSN-based secure multimedia communication framework to enhance video transmission quality, reduce energy consumption, and guarantee security. Due to the limitation of computing and energy resource, selective encryption and resource allocation naturally fits well to WSN for two reasons. First, selective encryption significantly reduces computational load by only controlling a tiny portion of the position information in multimedia stream structure. The magnitude information is useless in decryption without correct position information in the bit stream. Second, WSN has extremely limited energy resource which requires high communication energy efficiency by UEP-based WSN resource allocation. The contribution of this research is twofold. First, the proposed application layer selective encryption improves the robustness against transmission error by minimizing the encryption overhead among video frames. Second, the proposed network resource allocation scheme at the lower layers enhances the energy-distortion-encryption performance by distributing energy resources and communication efforts efficiently. The rest of the paper is outlined as follows. In Section II, we formulate the overall energy-distortion-encryption problem using a cross-layer approach for video packet streaming in WSNs. In Section III, we perform video decoding and encryption analysis, and decompose the overall design problem into subproblems. In Section IV, we propose a channel-aware selective encryption scheme with minimized video inter-frame dependency. Section V details the lower layer resource allocation based on UEP to provide energy-quality gains. In Section VI, we show our simulation results, followed by the conclusions presented in Section VII. The nomenclatures used in this paper are summarized in Table I.


TABLE I NOTATIONS AND SYMBOLS USED IN THIS PAPER

II. PROBLEM FORMULATION In this section, we propose a resource allocation and selective encryption framework for video streaming over WSNs. The framework can be formulated as a transmission quality optimization problem with security and energy consumption concan be adjusted and straints. The encryption block length the critical codeword in each video frame can be selected properly when we control the resource allocation parameters such . The as rate, retry and transmit power, etc., expected video quality after decryption and decoding should be maximized as

(1) subject to total encryption bit budget constraint as

(2) and minimum security constraint for the encrypted frames as

(3)

WANG et al.: ON ENERGY EFFICIENT ENCRYPTION FOR VIDEO STREAMING IN WIRELESS SENSOR NETWORKS

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as well as the total energy consumption constraint, or (4) It is noted that the encryption strength is determined according to the key length in a way very similar to [16], where a longer key length has a stronger resistance against key space reduction attacks. In addition, the security strength is also restrained by the total number of encrypted bits in each decoding unit, i.e., video frame. Typically, encryption using a stronger block encryption algorithm with a longer key length provides a higher security level. Encryption cost can be reduced either by reducing algorithm complexity or shortening key length. However, both approaches raise higher risk for information compromise. In this paper, we explore a new approach to provide multimedia security by controlling the crucial structure of the multimedia stream. The encryption cost is lowered by reducing the total encryption workload, rather than reducing key length or algorithm complexity. We use strong block-based encryption algorithms (such as advanced encryption systems—AES) and a minimum key length requirement as illustrated in (3) to provide security and the cost is reduced by using less encrypted multimedia information bits. In the later sections, we will analyze and exploit this problem to achieve video transmission quality, security, and energy efficiency jointly. We decompose the overall problem into several subproblems for both application layer priority-based selective encryption and cross-layer quality-driven distortion-energy minimization.

Fig. 1. Illustration of the extra encryption dependency in addition to the decoding dependency.

where the packet error rate can be estimated according to the frame length and channel bit error rate , or (6) The energy-distortion performance of each video frame transmitted in a wireless channel can be mapped directly from the network resource allocation strategy, i.e., (7)

III. SELECTIVE ENCRYPTION AND RESOURCE ALLOCATION Decoding dependency of video frames in the compressed encoded stream could result in a significant quality degradation in error-prone wireless channels. In addition, the selective encryption on certain important codewords in each video frame introduces further inter-frame dependency. Thus, designing a proper and efficient selective encryption algorithm to minimize the extra encryption dependency overhead is a critical issue for secure packet video transmission over WSNs. Fig. 1 illustrates the decoding dependency and extra encryption dependency for a typical H.264 video frame sequence, in which the encoder compresses video sequences in an order of I-P-B. The decoding of the I frame (Intra frame) is independent of any other frames, and the decoding of the P frame (Predicted frame) depends on the successful decoding of the I frame. Furthermore, the decoding of the B frame (Bidirectional frame) relies on the successful decoding of both I and P frames. More importantly, with the selective encryption under consideration, the decoding of each frame depends further on the successful decoding of the encryption blocks. Due to the nature of encryption, an encrypted block can be decrypted only if every bit in the code block is transmitted error-free. This dependency analysis can be summarized as follows:

(5)

In the final code stream, several factors can be modified and optimized adaptively. The decoding dependency and distortion reduction contribution, or

are governed by the encoder and employed video compression standard, such as AVC/H.264. The encryption dependency may significantly reduce the error resilience and expected distortion reduction, which should be minimized by designing the selective encryption algorithm properly. This minimization can be expressed equivalently as maximizing the successful transmission probability for all encryption-dependent video frames, i.e.,

Furthermore, the selection of minimal acceptable encryption block length is limited by the available encryption bit budget. Finally, the network resource allocation strategy should be optimized according to the decoding/encryption importance. This can be accomplished in an unequal error protection (UEP) fashion. The resource allocation parameters set , including rate , retry limit , and power for each video frame, are all

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related to the packet error rate and the energy consumption . By protecting those important secret blocks and I frames with an increased communication resource allocation, a significant energy saving can be achieved with improved video quality. In the next two sections, we will discuss the proposed channel-aware selective encryption algorithm in detail. IV. APPLICATION LAYER LOSS-AWARE SELECTIVE ENCRYPTION A. Determination of Encryption Block Length We first determine the minimum encryption block length to meet the security requirement. In general, a greater key length and hence a larger encryption block length lead to better security and privacy guarantee. However, the penalty of increasing encryption key length involves extra encryption dependency in video stream data and degrades video transmission quality in wireless channels. In other words, a longer encryption block length may potentially involve more correlated encryption position chunks belonging to multiple video frames, resulting in reduced decoding error resilience in wireless channels. Based on the above analysis, we propose a simple scheme to determine the shortest encryption key length while still assuring the secu, as shown in the following rity requirement specified by is equation. It is worth noting that the security requirement a system input parameter set by user, denoting the minimum security requirement in terms of key length and encryption block length, or

(8) In the proposed scheme, the ideal encryption key length is acquired by ceiling the number of minimal security requirement bits to the lowest exponential value of a binary two. In this way, the security requirement can be fulfilled while the extra inter-dependency cost can be reduced considerably. B. Frame Level Dependency Prioritization The second step of the proposed scheme is to determine the encryption priority among the video frames. For this purpose, we perform a complete frame level decoding dependency analysis as follows. In the compressed video streams, the decoding of I frames is independent of any other video frames, and the I frames in terms of distortion reduction as well as decoding dependency dominance are of paramount importance in a group of pictures (GOP). The decoding of P frames depends on the decoding of the previous I frames and/or P frames, while the decoding of the B frames depends on decoding both preceding I and P frames and the succeeding P frames. A typical IBPbased video frame decoding dependency graph is illustrated in Fig. 2. It is desirable to encrypt all of the important codewords in I-frames and P-frames when the encryption bit budget is enough. However, when the encryption bit budget is limited, a priority must be established to make the best use of limited encryption resource: only the codewords in the high priority video frames will be encrypted for cost-efficiency consideration.

Fig. 2. Example of frame level prioritization according to distortion reduction and decoding dependency.

Let denote the importance weight of one video frame in the be the distortion reduction measured in terms of GOP, and the square unit of MSE, which can be estimated exclusively in the encoding process. denotes the set of decoding descendent video frames in the GOP. The importance weight of one video frame can be expressed as follows: (9) This algorithm sorts the video frames according to their weights, which represent how many succeeding frames that depend on the current video frame’s decoding and how important those succeeding frames are, in terms of distortion reduction contribution. Given the decoding dependency graph and the distortion reduction requirement, the importance weight for each video frame can be easily identified. For example, the weight of the I since the decoding of all other frames frame in Fig. 2 is depends on the I frame. The importance weight of the second P because the decoding of the frame in Fig. 2 is then preceding B frame depends also on the decoding of the second P frame. If there are multiple P-frames, all of which directly depend on I-frame decoding, the priority among those P-frames should be determined by how many further B or P frames that depend on the decoding of the current P-frame, and what is the distortion reduction summation of all these dependent B or P frames. In the proposed selective encryption scheme, a frame level priority list is simply created and organized as shown in Algorithm 1 below. The encryption bit budget is granularly allocated in a coarse manner, according to the frame level priority list. Thus, the limited encryption bit budgets are efficiently allocated to the most important frames in terms of distortion reduction and decoding dependency, and the video stream structure is controlled without incurring much overhead. Algorithm 1: Frame level priority list creation

1) Read input parameters and priority list is set to empty, or

for each frame . Output .


2) For each video frame indexed by , do Steps 3–4. 3) Calculate the importance weight according to . . 4) Insert the calculated weight to the list 5) Sort the list in a descending order regarding to the magnitude of importance weight. 6) The algorithm ends. Output the priority list .

If (Codeword

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is in the intra-prediction Mode)

Put codeword in the plaintext encryption buffer where denotes a concatenation operation.

i.e.,

Add codeword priority list , i.e.,

offset and bit length .

,

in the

If (Codeword presents a nonzero magnitude coefficient value)

C. Selective Encryption of Critical Codeword Candidates In this subsection, we determine the codeword level encryp(i.e., the ordered encryption codeword set) tion priority list for each video frame , with the following objective:

Put codeword Add codeword priority list , i.e.,

in the encryption buffer

.


in the

Update the number of encrypted bits. (10) which is to minimize the extra encryption dependency implicitly expressed in (5). By decreasing the extra encryption dependency as illustrated in (10), the expectation of distortion reduction (i.e., the decoded and decrypted video quality) can be improved for transmissions over an error-prone channel. The proposed methodology for creating the codeword level selective encryption priority list is illustrated in Algorithm 2. In this paper, we use H.264 as the example of video codec to illustrate the proposed codeword level selective encryption, where the macro blocks in H.264 have sizes of 16 16, 16 8, 8 16, or 8 8. The codeword selection is performed in a way very similar to [6], primarily including intra-prediction mode, inter-prediction motion vector, and residue DCT coefficient levels. In the final entropy coding stage, transform domain coefficients will be quantized and mapped to codeword according to the codebook. Those large magnitudes coefficients typically contains more content information than small value coefficients in transform domain. Thus, those large value coefficients should also be encrypted when the encryption bit budget allows it. It is also noted that the encryption process is embedded in the video encoding/compression algorithm. Algorithm 2: Codeword level selective encryption priority list creation 1) Initialize the empty codeword priority lists

.

2) If the current video frame is an I frame, do Steps 3–5; else, do Steps 6–8. 3) Create the priority list for Intra-predicted frames. denotes the number of horizontal macro blocks, and denotes the number of vertical macro blocks. For For

to

do { to

do {

Perform intra-prediction and Exp-Golomb as well as CAVLC entropy coding. Consult the codeword in the entropy coding table.

of the current symbol/context

If the number of encrypted bits is less than the encryption bit budget, continue processing the next macro block. } } 4) Perform block-based encryption according to encryption bit budget and form the ciphered blocks , i.e., . If the size of the last encryption block bit is less than the encryption block size, pad zero bits and record the number of padding bits in the priority list. 5) Done. Output selective encryption priority list frames.

of I

6) Create the priority list for Inter-predicted frames. This is achieved by motion estimation analysis and residue entropy encoding. For

to

For

do { to

do {

Perform inter-prediction of the current macro block. Perform Exp-Golomb coding on the motion vectors and CAVLC entropy coding on the residues of macro blocks. Search the area matching to the current macro block with minimum MSE, and estimate the motion vectors. Identify the codeword of the current motion vectors in the Exp-Golomb coding table. i.e.,

Put codeword .

list

Add codeword , i.e.,

in the plaintext encryption buffer offset and bit length .

,

in the priority

Encode the residue macro block using CAVLC. Search for the codeword If (codeword presents a nonzero magnitude coefficient value) Put codeword

in the encryption buffer

.

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Add codeword priority list , i.e.,


(where transmit power control parameters play a dominating role):

in the

Update the number of encrypted bits. If the number of encrypted bits is less than the encryption bit budget, continue processing the next macro block. }

(11) and the transmit power at a certain desirable bit error rate and certain channel conditions is estimated as follows [20]–[23]:

} 7) Perform block-based encryption according to encryption bit budget in a way similar to Step 4, and form the ciphered blocks , i.e., . If the last encryption block bit is less than the encryption block, add padding zero bits and record the number of padding bits in the priority list. 8) Done. Output selective encryption priority list Inter-predicted frames.

of

It is noted that in this paper, we focus on multimedia content information security rather than network protocol security, and thus, only multimedia content privacy is considered. To achieve the content privacy, we use strong block-based encryption algorithm such as AES directly rather than designing mathematical encryption algorithms, so that the security is guaranteed by the strong encryption algorithm. One of the potential challenging attacks for the proposed selective encryption approach is brute force attack to crack the encrypted information bit. To counteract the brute force attack, larger encryption keys and bigger encryption blocks are used for higher security resilience. However, larger encryption keys and blocks lead to higher dependency among encrypted video frames, further decreasing the error-resilience in lossy wireless channel. This challenge is solved by network resource allocation as described in the next section. V. LOWER LAYER RESOURCE ALLOCATION A. Energy-Distortion Modeling The energy-distortion performance modeling for a single video frame transmission in wireless channels can be modeled as a resource allocation problem similar to the approach suggested in [19] and [20]. Both the frame delivery energy consumption and the frame delivery packet loss rate are related to the resource allocation parameters in a closed form. Since there are typically a large number of video frames in a GOP and many resource allocation parameters are applicable and adjustable, a complete and accurate global optimum is hard to achieve with the limited computational resources at sensor nodes. Thus, we simplify the resource allocation strategy by reducing the algorithmic complexity to approximate the optimal solution with the focus on power control. Specifically, the transmit power is identified as the dominating resource allocation parameter while retry limits and transmission rates are secondary factors. The total energy consumption of delivering a single video frame with length can be expressed as follows

(12) The energy consumption and the packet error rate of each video frame delivery are both modeled in a closed form which can be adjusted flexibly by transmit power scaling. B. UEP-Based Resource Allocation The UEP-based network resource allocation for the selectively encrypted video frames in a GOP can be further simplified and categorized. The cross-layer resource allocation approach is simplified to find the optimal transmission parameters for each I, P, and B frame class. The resource allocation parameter for each video frame is reduced to in the inter-correlated code stream. To sum up, we propose a simplified evolution-based algorithm to approximate the global optimal resource allocation strategy. The proposed algorithm is illustrated in Algorithm 3. Algorithm 3: Simplified evolution-based algorithm for resource allocation optimization

1) Read input parameters: distortion reduction and frame , channel state length of each video frame which dictate the decoding information , , and dependency as well as the encryption dependency. Initialize the buffer of output transmission parameters . 2) Perform the translation of transmission strategy parameters. The transmit power in the resource allocation parameter set can be substituted equivalently as a , given the channel state desirable bit error rate information . 3) Create the first generation population in a random way. . Define the Code each chromosome using maximum number of the iterations and start the iteration loop from Step 4 to Step 7. in the current population 4) Upon each element array, calculate the post decryption and decoding according to the distortion reduction expectation input parameters. for 5) Calculate the total energy consumption delivering the whole GOP video stream, using the input parameters and transmit power calculated from each in the current population. chromosome 6) Determine the fitness indicator for each chromosome .


7) Create the next generation population based on the probabilistic crossing between chromosome pairs. The probability of the crossing over is proportional to the fitness value. 8) If the number of loops is still less than the maximum iterations, go to Step 4 for chromosome refinement; otherwise, go to Step 9 to output the solution. 9) Output the final solution of chromosome in the last generation with the highest distortion and legitimate energy reduction expectation value . consumption Block-based encryption may introduce extra dependency among encrypted blocks, since bit errors incurred by wireless transmission will be propagated through the whole encrypted blocks. Such bit errors will cause serious packet loss for video frame transmission in a wireless environment, and lead to considerable amount of distortion in media decoding. To provide multimedia service quality either forward error correction (FEC)-based channel coding or UEP-based network resource allocation (e.g., transmission power) can be used to allocate communication protection unequally among encrypted blocks. In this paper, we design this strategy in a cross-layer design fashion. To provide multimedia service quality in wireless environment, the encryption blocks at application layer perform bit padding to reduce decoding dependency among encrypted video frames. The lower layer network resource allocation parameters are also optimized with regards to different perceptional importance levels of I-B-P-frames. There is also a tradeoff between encryption block length and the decoding latency. In case of one encryption block containing encrypted codewords from multiple frames, decoding latency occurs: the decoding of one video frame depends on the arrival of all the other video frames associated with the encryption block. Using smaller encryption blocks reduces the possibility of such decoding latency. However, the security level also decreases. This paper deals with secure multimedia transmission in wireless sensor networks, which provide multimedia service quality, content information security, and communication energy efficiency simultaneously. So in this paper, the energy efficiency is the primary consideration rather than decoding latency due to the extremely limited energy resource in sensor networks. VI. SIMULATIONS AND DISCUSSIONS We performed simulation experiments to evaluate the encryption and distortion performance gain from the proposed channel-aware selective encryption scheme for wireless packet video transmission compared with the traditional non-channel-aware schemes. The selective encryption overhead, in terms of the number of selectively encrypted bits and the ratio of encrypted bits to total bits, was also evaluated. The simulation setting parameters are stated as follows. In this simulation study, we explore the energy efficiency and media transmission quality on one WSN channel in a cross-layer fashion. At application layer, the H.264/AVC reference codec model was utilized to compress and packetize the video frames. The well-known foreman video sequence

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with 176 144 QCIF format was selected as the sample input video signal. The video compression frame rate is 30 fps with intra-prediction, inter-prediction, and bi-prediction frames. The context adaptive variable length (CAVLC) coding scheme was used as the entropy coding engine, and strong block-based AES scheme for encryption was utilized to secure the media content. The default security requirement is to use the encryption codeword with 256 bit key length. At lower layers, the bit error to , and rate in a wireless channel is scaled from the total encryption bit budget is scaled from 7000 to 11 500 bits. The link layer frame header is 6 bytes and the default channel loss is 130 dB. The channel bandwidth is 1 MHz and the receiver circuit power consumption is 15 MW. The encryption bit overhead is analyzed and illustrated in Fig. 3. The left subfigure shows the number of total encrypted bits versus the number of effectively encrypted bits in each selective encryption category. The right subfigure illustrates the percentage of encrypted bits out of the total coded stream, as well as the ratio of the encrypted bits to each video frame length. In the compressed I frame bit stream, those important codeword bits include the intra-prediction mode bits and the intra-prediction residue DCT coefficient bits. In the compressed B or P video frames, the important codeword bits include the motion vectors and the inter-prediction residue DCT coefficient bits. Typically, I frames have longer frame lengths and more residue DCT coefficient levels needed to be encrypted than B/P frames. This is because the decoding of an intra-predicted I frame depends only on the successful decoding of the I frame itself. The B or P frame, on the other hand, improves the compression performance by referring to the previous/bidirectional frames. According to the simulation results, the important codeword information accounts for 23.1% of the total bit stream for encrypting the intra-predicted I frame. For P or B frames with more efficient inter-prediction, a percentage of 7.6% and 1.8% of the total bit stream are achieved for effective encryption, respectively. Thus, encryption-distortion performance gain is achieved by our proposed channel-aware priority-based selective encryption scheme. Fig. 4 illustrates the end-to-end communication quality, i.e., the distortion expectation after transmission, decryption, and decoding in a wireless channel. It is clearly shown that the encryption-distortion gain of the proposed scheme is significant compared with the traditional scheme, especially in a situation with higher bit error rates. This is because in the traditional schemes, the selective encryption algorithm disregards the potential transmission bit errors and packet losses in wireless transmission scenarios. In our proposed scheme, on the other hand, the packet loss factor of the wireless channel has been considered in the selective encryption, and the encryption dependency overhead has been minimized. The quality performance gain for our scheme is prominent, up to 7 dB in terms of PSNR, especially under severe channel conditions with higher channel bit error rates. The distortion expectation performance with variable encryption bit budgets in various channel conditions is illustrated in Fig. 5. Large encryption bit budgets are typically favorable for increasing the media content privacy. However, a larger encryption bit budget potentially involves more correlated video frames, and thus, it can significantly

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Fig. 3. Selective encryption bit overhead analysis for a typical foreman QCIF IPB video sequence.

Fig. 4. End-to-end distortion performance with selective encryption in wireless channels.

decrease decrypted and decoded video quality in wireless channels. In our proposed scheme, the extra encryption dependency is minimized and a quality gain of up to 6–7 dB is achieved. The proposed selective encryption scheme in the application layer minimizes the encryption dependency among the video frames. The energy-distortion-encryption performance gain is further achieved by UEP-based resource allocation at the lower layers in sensor networks. Fig. 6 illustrates the comparison between the video frame delivery performances in error prone channels with typical UEP and non-UEP (i.e., equal error protection—EEP)-based transmission strategies. With UEP resource allocation and optimized transmit power control, the packet error rate of important I frames decreases while the packet error rates for unimportant B and P frames slightly increase compared to EEP scheme. However, by means of UEP resource allocation, the overheads of the increased P and B frame error rates are fully compensated by the advantages of the decreased I frame error rates in terms of the total distortion reduction.


Fig. 5. End-to-end distortion performance with variable selective encryption bit budgets.

Fig. 6. Frame error rate performance with UEP-based resource allocation and non-UEP-based resource allocation for a typical 0.008 J energy budget scenario.

Fig. 7 shows the energy-distortion-encryption performance gain of the proposed UEP-based resource allocation approach working in conjunction with the proposed selective encryption scheme. With EEP-based resource allocation, the communication resources such as transmit power are allocated equally among the video frames without considering the visual decoding importance. Furthermore, the traditional encryption scheme introduces a large additional encryption dependency overhead, and these extra dependencies may severely decrease the error resilience of the entire video stream transmission. With the UEP-based resource allocation, the more important video frames in terms of distortion reduction and decoding dependency are delivered with higher communication energy as an effort to improve quality gain. Those unimportant frames are transmitted with conservative network resource parameters to save communication energy. The proposed encryption scheme reduces extra encryption dependency overhead significantly, leading to an error resilience improvement. A considerably high energy-distortion gain up to 9 dB is achieved by performing


Fig. 7. End-to-end distortion with UEP-based resource allocation and nonUEP-based resource adaptation.

network resource allocation differently amongst different frames. VII. CONCLUSION In this paper, we have proposed a quality-driven approach to jointly optimize the energy, distortion, and encryption performance of video delivery over wireless sensor networks. The contribution of the proposed approach is twofold. At the application layer, a new wireless channel-aware selective encryption scheme is proposed to minimize the extra encryption dependency and to improve the video quality, especially in channels with a relatively high bit error rate. In addition, a new UEP-based network resource allocation scheme is proposed to optimize the selectively encrypted video transmission at the lower layers. Performance evaluation based on our simulation experiments has demonstrated a significant energy-distortion-encryption performance gain from the proposed approach compared with the existing techniques for encrypted wireless video streaming. REFERENCES [1] The ITU-T H.264 Standard and the ISO/IEC MPEG-4 Part 10 Standard (ISO/IEC 14496-10), May 2003. [2] G.-J. Sullivan and T. Wiegand, “Video compression—From concepts to the H.264/AVC standard,” Proc. IEEE, vol. 93, no. 1, pp. 18–31, Jan. 2005. [3] T. Wiegand, G.-J. Sullivan, G. Bjontegaard, and A. Luthra, “Overview of the H.264/AVC video coding standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 560–576, Jul. 2003. [4] T. Lookabaugh and D.-C. Sicker, “Selective encryption for consumer applications,” IEEE Commun. Mag., vol. 42, no. 5, pp. 124–129, May 2004. [5] M. Yang, N. Bourbakis, and S. Li, “Data-image-video encryption,” IEEE Potentials, vol. 23, no. 2, pp. 28–34, Aug. 2004. [6] S. Lian, Z. Liu, Z. Ren, and H. Wang, “Secure advanced video coding based on selective encryption algorithms,” IEEE Trans. Consum. Electron., vol. 52, no. 2, pp. 621–629, May 2006. [7] M. Kankanhalli and T. Guan, “Compressed domain scrambler/descrambler for digital video,” IEEE Trans. Consum. Electron., vol. 48, no. 2, pp. 356–365, May 2002.

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[8] W. Zeng and S. Lei, “Efficient frequency domain selective scrambling of digital video,” IEEE Trans. Multimedia, vol. 5, no. 1, pp. 118–129, Mar. 2003. [9] H. Cheng and X. Li, “Partial encryption of compressed images and videos,” IEEE Trans. Signal Process., vol. 48, no. 8, pp. 2439–2451, Aug. 2000. [10] M. Podesser, H.-P. Schmidt, and A. Uhl, “Selective bitplane encryption for secure transmission of image data in mobile environments,” in Proc. 5th Nordic Signal Processing Symp., Oct. 2002. [11] L. Tang, “Methods for encrypting and decrypting MPEG video data efficiently,” in Proc. Int. Conf. ACM Multimedia, Nov. 1996, pp. 219–229. [12] M. Grangetto, E. Magli, and G. Olmo, “Multimedia selective encryption by means of randomized arithmetic coding,” IEEE Trans. Multimedia, vol. 8, no. 5, pp. 905–917, Oct. 2006. [13] C. Wu and C. Kuo, “Design of integrated multimedia compression and encryption systems,” IEEE Trans. Multimedia, vol. 7, no. 5, pp. 828–839, Oct. 2005. [14] O. Au, J. Zhou, Y. Chen, and Z. Liang, “Security analysis of multimedia encryption schemes based on multiple Huffman table,” IEEE Signal Process. Lett., vol. 14, no. 3, pp. 201–204, Mar. 2007. [15] G. Jakimoski and K. Subbalakshmi, “Cryptanalysis of some multimedia encryption schemes,” IEEE Trans. Multimedia, vol. 10, no. 3, pp. 330–337, Apr. 2008. [16] C. Nanjunda, M.-A. Haleem, and R. Chandramouli, “Robust encryption for secure image transmission over wireless channels,” in Proc. IEEE Int. Conf. Commun., May 2005, vol. 2, pp. 1287–1291. [17] W. Wang, D. Peng, H. Wang, and H. Sharif, “An adaptive approach for image encryption and secure transmission over multirate wireless sensor networks,” in Wireless Commun. Mobile Comput. J. (WCMC). New York: Wiley, 2009, vol. 9, pp. 383–393. [18] W. Wang, D. Peng, H. Wang, H. Sharif, and H.-H. Chen, “Energy-constrained quality optimization for secure image transmission in wireless sensor networks,” in Advances in Multimedia (AM). New York: Hindawi, 2007, vol. 2007, 9 pp. [19] W. Wang, D. Peng, H. Wang, H. Sharif, and H.-H. Chen, “Energyconstrained distortion reduction optimization for wavelet-based coded image transmission in wireless sensor networks,” IEEE Trans. Multimedia, vol. 10, no. 6, pp. 1169–1180, Oct. 2008. [20] W. Wang, D. Peng, H. Wang, H. Sharif, and H.-H. Chen, “Matching stream authentication and resource allocation to multimedia codec dependency with position-value partitioning in wireless multimedia sensor networks,” in Proc. IEEE Globecom, Dec. 2009. [21] C. Schurgers, O. Aberthorne, and M.-B. Srivastava, “Modulation scaling for energy aware communication systems,” in Proc. Int. Symp. Low Power Electronics and Design, Aug. 2001, pp. 96–99. [22] S. Haykin, Communication System, 3rd ed. New York: Wiley, 1994, pp. 510–553. [23] W. Stallings, Data and Computer Communications, 7th ed. Upper Saddle River, NJ: Prentice-Hall, 2000, pp. 85–86. [24] A.-M. Alattar, G.-I. Al-Regib, and S.-A. Al-Semari, “Improved selective encryption techniques for secure transmission of MPEG video bit-streams,” in Proc. IEEE Int. Conf. Image Processing, Oct. 1999, vol. 4, pp. 256–260. [25] I. Agi and L. Gong, “An empirical study of secure MPEG video transmissions,” in Proc. IEEE Symp. Network and Distributed System Security, Feb. 1996, pp. 137–144.

Wei Wang (M’10) received the B.S. and M.S. degrees from Xian Jiaotong University, Xian, China, in 2002 and 2005 respectively, and the Ph.D. degree from the University of Nebraska-Lincoln in 2009. He is currently an Assistant Professor in the Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings. His research interests include computer networks, wireless communications, embedded systems, and multimedia computing. Dr. Wang is one of the recipients of the Best Paper Award in IEEE WCNC 2008. He also severed as a TPC member in IEEE Globecom 2010, WiMob 2008 and 2009, and IEEE WCNC 2009, as well as a reviewer for several journals/transactions.

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Michael Hempel (M’07) received the Ph.D. degree in computer engineering from the University of Nebraska-Lincoln in 2007. He is currently a Research Assistant Professor in the Department of Computer and Electronics Engineering, University of Nebraska-Lincoln. His research interests include wireless communications networks and multimedia communications.

Dongming Peng (M’06) received the B.A. and M.A. degrees in electrical engineering from Beijing University of Aeronautics and Astronautics, Beijing, China, in 1993 and 1996, respectively, and the Ph.D. degree in computer engineering from Texas A&M University, College Station, in 2003. From 1996 to 1997, he was a faculty member at Beijing University. In 2002, he joined the University of Nebraska-Lincoln, where he is currently an Associate Professor. His research interests include digital image processing, computer architectures, parallel and distributed computing, and sensor network. Dr. Peng is one of the recipients of the Best Paper Award in IEEE WCNC 2008. He has also served as a referee and program committee member for several conferences and journals.

Honggang Wang (M’10) received the B.S. and M.S. degrees from Southwest Jiaotong University, China, in 1996 and 2001, respectively, and the Ph.D. degree from University of Nebraska-Lincoln in 2009. He worked for Lucent Technologies China from 2001 to 2004 as a Senior Software Developer. He is currently an Assistant Professor at the University of Massachusetts, Dartmouth. His research interests include networking, wireless sensor networks, multimedia communication, network and information security, embedded sensory system, biomedical computing, and pattern recognition.

Hamid Sharif (SM’06) received the B.Sc. degree from the University of Iowa, Iowa City, the M.Sc. degree from the University of Missouri, Columbia, and the Ph.D. degree from the University of Nebraska-Lincoln, all in electrical engineering. He is the Paul and Betty Henson Professor of Computer and Electronics Engineering Department at the University of Nebraska-Lincoln (UNL). He is also the Director of Advanced Telecommunications Engineering Laboratory (TEL) at UNL. His research areas include: wireless communication protocols, wireless sensor networks, communication system performance modeling, and communication and network security. He has authored/coauthored a large number of journal and conference papers. Dr. Sharif is serving as an editor for several journals including the Co-Editor-in-Chief of Wiley’s Security and Communication Networks journal. He has contributed to the IEEE in many roles including Chair of the Nebraska Section, Chair of the Nebraska Computer and Communication Chapters, and currently, he is the Chapter Coordinator for the IEEE Region 4.

Hsiao-Hwa Chen (F’10) received the B.Sc. and M.Sc. degrees from Zhejiang University, Hangzhou, China, and the Ph.D. degree from the University of Oulu, Oulu, Finland, in 1982, 1985, and 1991, respectively. He is currently a full Professor in the Department of Engineering Science, National Cheng Kung University, Taiwan. He has authored or coauthored over 250 technical papers in major international journals and conferences, five books, and three book chapters in the areas of communications. Dr. Chen served as general chair, TPC chair, and symposium chair for many international conferences. He served or is serving as an Editor or/and Guest Editor for numerous technical journals. He is the founding Editor-in-Chief of Wiley’s Security and Communication Networks Journal (http://www.interscience.wiley.com/journal/security). He is the recipient of the Best Paper Award in IEEE WCNC 2008.