On the performance of video resolution, motion and dynamism in transmission using near-capacity transceiver for wireless communication

entropy Article On the Performance of Video Resolution, Motion and Dynamism in Transmission Using Near-Capacity Transceiver for Wireless Communication Nasru Minallah 1,†, Khadem Ullah 1,† , Jaroslav Frnda 2,† , Laiq Hasan 1,† and Jan Nedoma 3,*,†   Citation: Minallah, N.; Ullah, K.; Frnda, J.; Hasan, L.; Nedoma, J. On the Performance of Video Resolution, Motion and Dynamism in Transmission Using Near-Capacity Transceiver for Wireless Communication. Entropy 2021,23, 562. https://doi.org/10.3390/ e23050562 Academic Editor: Jorge Rocha Received: 21 April 2021 Accepted: 27 April 2021 Published: 1 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan; [email protected] (N.M.); [email protected] (K.U.); [email protected] (L.H.) 2Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 010 26 Zilina, Slovakia; jaroslav[email protected] 3Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 17. Listopadu 15, 708 33 Ostrava-Poruba, Czech Republic *Correspondence: [email protected] † All authors contributed equally to this work. Abstract: This article investigates the performance of various sophisticated channel coding and transmission schemes for achieving reliable transmission of a highly compressed video stream. Novel error protection schemes including Non-Convergent Coding (NCC) scheme, Non-Convergent Coding assisted with Differential Space Time Spreading (DSTS) and Sphere Packing (SP) modulation (NCDSTS-SP) scheme and Convergent Coding assisted with DSTS and SP modulation (CDSTS-SP) are analyzed using Bit Error Ratio (BER) and Peak Signal to Noise Ratio (PSNR) performance metrics. Furthermore, error reduction is achieved using sophisticated transceiver comprising SP modulation technique assisted by Differential Space Time Spreading. The performance of the iterative Soft Bit Source Decoding (SBSD) in combination with channel codes is analyzed using various error protection setups by allocating consistent overall bit-rate budget. Additionally, the iterative behavior of SBSD assisted RSC decoder is analyzed with the aid of Extrinsic Information Transfer (EXIT) Chart in order to analyze the achievable turbo cliff of the iterative decoding process. The subjective and objective video quality performance of the proposed error protection schemes is analyzed while employing H.264 advanced video coding and H.265 high efficient video coding standards, while utilizing diverse video sequences having different resolution, motion and dynamism. It was observed that in the presence of noisy channel the low resolution videos outperforms its high resolution counterparts. Furthermore, it was observed that the performance of video sequence with low motion contents and dynamism outperforms relative to video sequence with high motion contents and dynamism. More specifically, it is observed that while utilizing H.265 video coding standard, the Non-Convergent Coding assisted with DSTS and SP modulation scheme with enhanced transmission mechanism results in Eb/N0 gain of 20 dB with reference to the Non-Convergent Coding and transmission mechanism at the objective PSNR value of 42 dB. It is important to mention that both the schemes have employed identical code rate. Furthermore, the Convergent Coding assisted with DSTS and SP modulation mechanism achieved superior performance with reference to the equivalent rate Non-Convergent Coding assisted with DSTS and SP modulation counterpart mechanism, with a performance gain of 16 dB at the objective PSNR grade of 42 dB. Moreover, it is observed that the maximum achievable PSNR gain through H.265 video coding standard is 45 dB, with a PSNR gain of 3 dB with reference to the identical code rate H.264 coding scheme. Keywords: H.265 High Efficient video Coding (HEVC); Sphere Packing (SP) Modulation; BER reduction; Differential Space Time Spreading (DSTS); Extrinsic Information Transfer (EXIT) Chart Entropy 2021,23, 562. https://doi.org/10.3390/e23050562 https://www.mdpi.com/journal/entropy Entropy 2021,23, 562 2 of 24 1. Introduction Video on-demand transmission is the major factor of the estimated increase in network traffic over the cellular network. By the end of 2023, the emerging increase in mobile applications downloading will reach 300 million and the 5G speeds will reach about 575 Mbps, which is 13 times higher than the average mobile connections. There will be a significant need in increase of bandwidth with the highest connectivity requirements of the future networks. A crucial factor leading the increase in mobile speed is the increasing proportion of 4G mobile connection and the rising interest in 5G connections [ 1 – 3 ]. The applications of Internet data traffic on 3G and 4G over wireless communication networks increases exponentially, which increases the needs of highly efficient networks providing superior connectivity, reliable and low latency communication [4]. For a raw video sequence with one pixel representation of a gray scale and color video, 8 and 24 bits are used correspondingly. A total of 944 Mbps is required to display one frame of a simple colored TV video with a resolution of 1024 × 768 pixels with a frame rate of 25 fps [ 5 ]. Bandwidth requirement is reduced by encoding the video content through source video coding standards such as H.264/AVC or H.265/HEVC for transmission over the communication network. However, the High Definition (HD) videos require higher bandwidth for transmission over communication channels, even with the employment of the video compression mechanism. For the very first time, Shannon proposed the basics of channel capacity bounds over a noisy channel providing minimum probability of errors by employing complex encoding systems and identified the error-resilient features of the communication systems [ 6 , 7 ]. Shannon’s coding theorem presented signals and messages as a point in space and developed a method that any communication system can be represented geometrically. After that researcher presented different techniques for achieving that limit. The first forward error correction block code for single error correction was the Hamming code [ 8 ]. Elias was the first who discovered error-correcting convolutional codes [ 9 , 10 ], in which the encoding dependencies are shifted from finite-length segments to encoding dependencies that exist over the entire block. The authors in [ 11 ] used convolutional codes for the burst of error correction in information bit-stream. In [ 12 ], sequential algorithms were proposed for decoding the convolutional codes. A heuristic discussion was presented in greater detail about probabilistic decoding [ 13 ]. Furthermore, the authors presented in depth analysis about A-priori, A-posteriori knowledge, mutual information, channel degradation, channel quantization and channel capacity i.e., different terminologies linked with designing an encoding and decoding schemes. The Viterbi Algorithm (VA) proposed in [ 14 ] is a significant achievement ever in the history of convolutional correction codes. VA primarily works on finding the closest sequence of the transmitted information bits (Maximum A-posteriori Probability (MAP) sequence estimation), which results in minimum Bit Error Ratio (BER) [ 15 , 16 ]. VA can also be used as a maximum likelihood sequence detector for AWGN channel, which finds applications in Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), and various other fields [ 16 , 17 ]. An efficient bidirectional search algorithm for computing the free distance of a convolutional codes is described in [ 18 ]. VA works on codeword error minimization for convolutional codes. Therefore, authors proposed the symbol based MAP algorithm in [ 19 ], minimizing error rate in symbols or bits through optimal decoding. However, compared to VA, the symbol based MAP decoding is only attractive for short block codes with short constraint lengths due to offering higher complexity algorithm and large storage for larger constraint lengths. Turbo codes are first presented in [ 20 , 21 ], which comprises the concatenation of two Recursive Systematic Convoluational (RSC) codes interfaced through an interleaver. Turbo codes are also used in third-generation (3G) mobile radio systems [ 22 ]. In turbo codes, an iterative algorithm is used at the decoder side to extract the transmitted information bits. The authors proposed Soft Output Viterbi Algorithm (SOVA) in [ 23 ]. They modified the VA in such a way that it gives the most probable transmitted sequence in a Markov finite-state Entropy 2021,23, 562 3 of 24 chain along with the reliability or posteriori information. Koch et.al in [ 12 ] proposed the Max Log MAP algorithm for turbo decoder with lower complexity than SOVA. Authors in [ 24 – 28 ] evaluated the performance of the Joint Source Channel Codes (JSCC) and it was concluded that it could be jointly optimized as a one pair for achieving a lower BER. Authors in [ 24 ] proposed an extended curve-fitting algorithm which finds optimal design criteria for JSCC. In [ 29 ], the authors used and extended the application of Fully Parallel Turbo Decoder (FPTD), for the implementation of a unary error correction on hardware considering the video transmission of information bits on JSCC. An analog low complexity JSCC system is designed [ 30 ] for the transmission of still images. Deep JSCC is proposed [ 31 ], which does not explicitly relay on source and channel encoder and instead trained an auto-encoder composed of two convolutional neural network and can be used in a non-trainable layer in the middle of a communication channel. In [ 32 ], the authors proposed a lower complexity Robust Distributed Video Coding (RDVC) framework to optimize the quality of video communication for wireless multimedia sensors networks. A new coding scheme is presented based on Wyner-Ziv coding for error resilience and Rate Distortion (RD) performance. In [ 33 ], the authors proposed systematic code Low Density Generator Matrix (LDGM), which under maximum likelihood decoding, can achieve the capacity of memoryless binary input/output channel. The extent of performance achievement in iterative decoding is determined from the number of profitable iterations by EXIT chart analysis in [ 34 ]. In [ 35 ], the authors showed the EXIT chart as a versatile tool for designing of different serial concatenation codes. The authors [ 36 ] proposed multiedge type EXIT chart bit mapping for low density parity check Bit-interleaved Coded Modulation (BiCM). A delightful source coding candidate for wireless video communication is the compression efficient standard H.264 [ 37 – 40 ]. In [ 41 ], the authors proposed a mobile model called Proposed Generation (Pro-G) for supporting large number of user applications, making usage of wider bandwidth, adoptive modulation and coding for cellular systems. Furthermore, transcoding technique (H.265 pro, incorporating two Highest Efficiency Video Coding (HEVC) structure) is used for adoptive video streaming, which results in providing multiple data rates of a video streams. Authors in [ 42 ] studied the compressed video transmission using JSCC based on the stream contents characteristics of HEVC. Clustering algorithm Fuzzy C-Means (FCM) from the field of artificial intelligence is used for reducing the noise effects and for classifications of wireless channel. A new Sample Adaptive Offset (SAO) in loop filter make feasible the higher compression efficiency of H.265/HEVC for both objective and subjective measures. Estimating the best SAO parameter per coding tree unit for low powered or real time encoders results in a number of issues such a high computational complexity or architectural inefficiency. The authors proposed in [ 43 ] various SAO policies reducing complexity and removing the inefficiency caused by its implementation. A broadcaster group from Japan plan to stream a television services based on the latest new standard HEVC having twice the compression capability compared to H.264. A studied has been carried out for estimating the required bit rates. Different multiformat videos were evaluated in [ 44 ] and suggested that a 10–15 Mbits/s is required for 1080/60/I and 1080/60/P, 30–40 Mbit/s is required for 2160/60/P, while 80–100 Mbit/s is needed for 4320/60/P format. The study deduce a conclusion that such videos can also be transmitted via the existing satellite channel bandwidth. The authors in [ 45 ] proposed a method for identifying the moving objects that passes through the specific region without fully decoding the bitstreams. Foreground prediction block is extracted from the video bit-stream according to the motion vector of H.265 and clustered into region of interests. The state of moving object can be find by match the moving objects and the region of interest in the current frame. HEVC chips are incorporated into the application processor system-on-a-chip within the mobile devices due to its wide usage in video transmission. However, the coding bandwidth accessed for the motion estimation (ME) operation within HEVC has changed over time due to the adaption of the intelligent power management with in the process system-on-a-chip [ 46 ]. The needs of on-demand coding bandwidth Entropy 2021,23, 562 4 of 24 for HEVC must be considered in designing a low power system. The authors proposed an intelligent ME controller algorithm model, which is coding bandwidth efficient and referred to a Dynamic Voltage Frequency Scale aware (DVFS-aware). DVFS-aware can be integrated in ME for coding bandwidth realization, coding bit-rate, and coding-qualityoptimized HEVC ME design. The authors in [ 47 ] proposed an algorithm for improving the bitrate (<; 1.5 %), while a slight decrease in PSNR (<; 0.15 decibel) has been reported. Taking in mind the above background, we propose an arrangement for the H.264 and HEVC compressed video bitstream transmission through Soft Bit Source Decoding (SBSD) scheme. The propose system uses H.264 and H.265 [ 48 ] as a source encoder for simulation purpose. Artificial redundancy is generated in the transmitter side using two different combination of Over Complete Mapping (OSM) and RSC encoder while keeping the overall bitrate constant. The redundancy is iteratively utilized in the decoder side for the enhancement of BER performance. Its hard to estimate an impulse response for each individual Multiple Input Multiple Output (MIMO) link in Rayleigh fading channel due to experiencing different fading. In order to further improve the performance of the system, a transmitter diversity gain technique is incorporated, such that the coded video bitstream is passed to Sphere Packing (SP) modulation and Differential Space Time Spreading (DSTS) scheme which overcome channel estimation dependency. Due to embedding DSTS MIMO scheme, the proposed transceivers can be deployed in diverse environments for providing better performance. The following is the main contributions of the proposed work while considered in designing of the three systems i.e., NCC, NCDSTS-SP and CDSTS-SP: • H.264 and H.265 source compression standard has been incorporated in order to visualize the quality of the highly compressed video stream at the receiver. • SP modulation are included in order to observe the performance of the proposed systems on fading channel. • SP modulation is inspired by space time modulation and provides the diversity and coding gain for the proposed system in order to efficiently estimate and recover the actual transmitted information. • DSTS scheme is included to remove the Channel State Information (CSI) estimation dependency and the receiver does not required to know the channel fade. • Different inner and outer code rates are used in the concerned schemes to visualize the effect of code rates on the convergence property of iterative schemes. • EXIT chart are used for finding the number of profitable iterations in the decoding process. • Diverse video sequences have been tested to measure the role of the static objects having fine details, dynamism and motion of the object in the background, on the performance metrics. • To the best of our knowledge, this is distinctive research study to transmit the highest compression efficient coded video using the H.265/HEVC standard while employing the advocated wireless transmission setup. The rest of the proposed manuscript has been structured as follows. In Section 2, preliminaries and system design criteria has been presented that is used in designing of our proposed systems. Section 3, a detailed analysis of the proposed system models has been briefly explained. Section 4presents the performance analysis of our propose simulation. Finally, a conclusion of the resultant work is provided in Section 5. 2. Preliminaries & System Design Criteria MIMO schemes are mainly used for multiplexing and diversity gain. In multiplexing, different uncorrelated symbols are transmitted from all the antennas while for providing diversity gain, the transmitted symbols should be correlated. Space Time Block Codes (STBC) performs encoding both in space domain as well as in time domain due to using two transmitting (Tx) antennas and requiring two period for transmitting the information symbols. The first Tx antenna transmit the original symbol and all the remaining Tx antennas are transmitting the linear combination or the conjugate version of the symbol Entropy 2021,23, 562 5 of 24 that are transmitting from the first antennas. In case of two Tx antennas, symbols d1 and d2 are transmitted at the first time period t1 while at time period t2 , −d∗ 2 and d∗ 1 are transmitted, for which the decoded matrix can be represented as [49]: d1−d∗ 2 d2d∗ 1 From the decoding matrix, the column contains the symbols transmitted at the corresponding time period while the rows comprises of the symbols transmitted from the specific Tx antenna. STBC with the following encoding matrix was proposed for four Tx antennas [50]. 1 √2·    d1−d∗ 2−z∗ 1−z∗ 2 d2d∗ 1z2−z1 d3−d∗ 4d∗ 1d∗ 2 d4d∗ 3−d2d1     (1) where d1 , d2 , d3 , and d4 are the data symbols, z1=Real(d3)−j imaginary(2d1d2d∗ 4) and z2=d∗ 1+d4+d2 2d∗ 4+d∗ 1d2x3−d∗ 1d2d3∗ . Differential Space Time Block Codes (DSTBC) does not required CSI estimation at both the Tx or receiving (Rx) antenna. In fast fading channel, the training sequences becomes outdated. Therefore, the overhead will increases if we are transmitting the training sequence at each instance. Differential scheme is suitable in case of fading channels. For PSK modulation having Msp signal points and spectral efficiency m=log2Msp , the symbols set Symt for a transmission period of t using differential PSK from the constellation are: Symt=exp(j2πψt Msp )(2) where ψte{ 0,1,2,3 . . . Msp − 1 } . Therefore, the differential encoder, encodes the symbol in such a way that the data information is the difference between the phases of the current and previous symbols such that dt=Symt·dt−1 . Similarly, DSTBC following the same method for two Tx antennas in transmitting the two symbols having phases dt+1 , dt+2 correspondingly, and the previous transmitted symbols dt−1 , dt . Therefore, in each block, the sum of the phases of new vector dt+1dt+2T and the previously transmitted vectors, dt−1dtTand −d∗ td∗ t−1T, is sent as follow [49]: dt+1 dt+2=d∗ t−1d∗ t −dtdt−1·Symt+1 Symt+2(3) The design criteria that guarantee maximum diversity and coding gain can be find [ 49 ], e.g., the signal received ( rsigj t ) at antenna j after demodulation is expressed as in the following equation [51]. rsignj t= N ∑ i=1 αi,jψi t√E+ηj t(4) Here, the codeword is expressed with ψj t and the path gain is represented with ηj t . The relationship in Equation (4) between input and output can be also referred to fading channel model. The codewords that are transmitting from a total of NTx antennas over the total duration of Ttime period transmission is given below. ψ1=      ψ1 1,1 ψ1 1,2 ··· ψ1 1,N ψ1 2,1 ψ1 2,2 ··· ψ1 2,N . . .. . ..... . . ψ1 T,1 ψ1 T,2 ··· ψ1 T,N       (5) Entropy 2021,23, 562 6 of 24 At the receiving side, the signal for a Ttime period of transmission with a total of M receiving antennas is expressed as in the following equation. rsigt=      rsig1,1 rsig1,2 ··· rsig1,M rsig2,1 rsig2,2 ··· rsig2,M . . .. . ..... . . rsigT,1 rsigT,2 ··· rsigT,M       (6) If the antennas are far away then the path gain is independent from each other, otherwise a spatial correlation exist that can be expressed with the following N×M channel matrix: H=     α1,1 α1,2 ··· α1,M α2,1 α2,2 ··· α2,M . . .. . ..... . . αN,1 αN,2 ··· αN,M      (7) r=ψ·H+N(8) where noise Nof a T×Nmatrix is expressed as given below. N=     η1,1 η1,2 ··· η1,M η2,1 η2,2 ··· η2,M . . .. . ..... . . ηT,1 ηT,2 ··· ηT,M      (9) An error occurred if codeword ψ1 is selected from the codebook at the transmitter antenna and the receiver estimates ψ2due to some noise present in the channel. ψ2=     ψ2 1,1 ψ2 1,2 ··· ψ2 1,N ψ2 2,1 ψ2 2,2 ··· ψ2 2,N . . .. . ..... . . ψ2 T,1 ψ2 T,2 ··· ψ2 T,N      (10) The probability of error using the union bound for a set of comprising L number of codewords is given below: Perror |ψ1is sent ≤ L ∑ i=2 Pψ1→ψi(11) For an error matrix D(ψ1 , ψ2) = ψ2−ψ1 , the pairwise error probability to be define [ 51 ] in term of non negatives reals number eigenvalues λn≥ 0 of matrix A(ψ1,ψ2)= D(ψ1,ψ2)H·D(ψ1,ψ2)=(ψ2−ψ1)H·(ψ2−ψ1) where His conjugate and transpose a the corresponding matrix. P(ψ→error)≤ r ∏ n=1 λn!−MEs 4N0 −rM (12) or Pψ1→ψ2≤(Es 4N0)−rM (∏r n=1λn)M(13) Entropy 2021,23, 562 7 of 24 or Pψ1→ψ2≤4rM (∏r n=1λn)MγrM (14) The rank of the matrix A(ψ1 , ψ2) represent the diversity gain of the space time codes which can be also expressed with the rank of the difference matrix D(ψ1 , ψ2) multiplied with the number of Mreceiving antennas, i.e., the power of SNR ( r×M ) in the denominator of Equation (14) . A rank cafeteria to be defined for guarantee a full diversity scheme as, if the matrix A(ψi , ψi) is a full rank matrix for all the possible codewords. Contrary to diversity gain, coding gain is the distance between two codes which is related to the determinate of matrix A(ψ 1, ψ 2 ) . Coding gain can be improve by maximizing the minimum determinate of matrix A(ψ1, ψ2)[49,51]. The Alamouti code is a full rank matrix and is satisfies the determinant criteria. Considering a time correlated Rayleigh fading channel, SP modulation obtaining a full diversity gain using the joint combination of orthogonal designs with sphere packing [ 52 ]. Let, the orthogonal function G 1 (d) = d1I1 , where I1 denotes the identity matrix, then the recursive orthogonal function is expressed as given below [52,53]: G2k(d1,··· ,dk+1)=G2k−1(d1,··· ,dk)dk+1I2k−1 −d∗ k+1I2k−1GH 2k−1(d1,··· ,dk)(15) where d1 , d2 , . . . dk+1 are the complex variables and their conjugate is expressed as d∗ 1 , d∗ 2 , . . . d∗ k+1,His the transpose and conjugate of G2k−1. Furthermore, the SP signals are constructed using the above equation as expressed in the following equation. Sp =q2k/(k+1)G2k(d1,d2,··· ,dk+1)(16) The symbol rate is denote as k+ 1 / 2 k where the p2k/(k+1) is used for energy constraint as a normalization factor. Using Equation (16) , the set of SP signals for 2 transmitting antennas are given below: Sp =q2k/(k+1)G2(d1,d2)=(17) q2k/(k+1)G1(d1)d2 −d∗ 2GH 1(d1)=q2k/(k+1)d1d2 −d∗ 2d∗ 1 Using the same Equation (16) , the set of SP signals can be constructed for four and eight transmitting antennas is given below: Sp =q2k/(k+1)G4(d1,d2,d3)=(18) q2k/(k+1)G2(d1,d2)d3I2 −d∗ 3I2GH 2(d1,d2) =q2k/(k+1)    d1d2d30 −d∗ 2d∗ 10d3 −d∗ 30d∗ 1−d2 0−d∗ 3d∗ 2d1     Entropy 2021,23, 562 8 of 24 Sp =q2k/(k+1)G8(d1,d2,d3,d4)=(19) q2k/(k+1)G4(d1,d2,d3)d4I4 −d∗ 4I4GH 4(d1,d2,d3) =q2k/(k+1)              d1d2d30d40 0 0 −d∗ 2d∗ 10d30d40 0 −d∗ 30d∗ 1−d20 0 d40 0−d∗ 3d∗ 2d10 0 0 d4 −d∗ 4000d∗ 1−d2−d30 0−d∗ 40 0 d∗ 2d10−d3 0 0 −d∗ 40d∗ 30d1d2 000−d∗ 40d∗ 3−d∗ 2d∗ 1              . The differential MIMO (DSTS) scheme comprises two encoders i.e., differential encoder and space time spreading (STS) encoder as shown in Figure 1. The symbols are first differential encoded and then spreaded by STS scheme. For the very first time, two dummy symbols ddiff1 0 , ddiff2 0 are transmitted which have no information bits. After that, the data information stream dk t ; k=1,2 are separated into ddiff1 t , ddiff2 t symbols. The encoded symbols at the 2 transmitter antennas are then expressed as with the following equation [54]. ddiff1 t=d1 t·ddiff1 t−1+x2 t·ddiff2∗ t−1 q|ddiff1 t−1|2+|ddiff2 t−1|2(20) ddiff2 t=d1 t·ddiff2 t−1−d2 t·ddiff2∗ t−1 q|ddiff1 t−1|2+|ddiff2 t−1|2(21) After passing from the STS encoder, the two consecutive symbols at both the transmitting antennas is expressed with the following mapping as shown in Figure 2[54]. dspreaded1 t=1 √2(¯ Sc1·ddiff1 t+¯ Sc2·ddiff2 t)(22) dspreaded2 t=1 √2(¯ Sc1·ddiff1 t−¯ Sc2·ddiff2 t)(23) where ¯ Sc1 and ¯ Sc2 are the orthogonal codes at both the transmitting antennas. The proposed work uses DSTS MIMO scheme for attaining diversity and coding gain. In case of Rayleigh fading fading channel, DSTS MIMO scheme is a suitable candidate for removing the CSI estimation at each instance of transmission. Entropy 2021,23, 562 9 of 24 Encoder Differential STS Encoder Delay dspreaded2 t dspreaded1 t ddiff t−1 ddiff t si Figure 1. Differential Space Time Spreading Scheme. D Odd substream Even substream × × × ×P ¯ Sc1 ¯ Sc2 ddif f t ddiff 1 t ddiff 2 t ddiff 1 t¯ Sc1 ddiff 2 t¯ Sc2 ddiff 2 t¯ Sc1 ddiff 1 t¯ Sc2 Figure 2. Space Time Spreading Technique. 3. Proposed System Model Typically, the primitive of the transmitter block comprises discrete input, encoder, digital modulator, and the output. Channel encoder is operating for the removal of error effects by adding some redundancy through a controlled methodology in the information bits. Channel flexibility takes place by manipulating RSC codes as a constituent channel codes to efficiently operate in AWGN and as well in Rayleigh fading channels. The preliminaries and system design criteria has been briefly explained in [ 26 ]. The proposed system model comprises three video phone arrangements as shown in Figure 3and the specifications of the transceivers are given in Tables 1–5. All the transceivers follow the same mechanism and are only different by using different modulation schemes and arrangements of inner and outer code rates. In the proposed scheme, video is initially compressed at the transmitter side by using the international compression standard H.264 and H.265 video codec with the parameters settings of the source encoder as given in Table 1. The different building blocks of the proposed system models are as follows. 3.1. H.264 and H.265 Source Video Coding Standard Advance Video Coding (AVC)/H.264 is a compression standard resulted in the extension of the video service delivery to various networks and applications. The coding architecture of H.264 consists of two basics layers i.e., Video Coding Layer (VCL) and Network Adaptation Layer (NAL). VCL provided efficient representation of video contents while NAL provides network friendly representation. On the other hand, HEVC is the latest state of the art video compression standard designed for achieving the goals of Entropy 2021,23, 562 16 of 24 Table 5. Analysis of different sampling formats and video sequences. Sampling Format Video Sequence Resolution Luminance (Y) Resolution Luminance Bits per Frame Chrominance (Cb & Cr) Resolution Chrominance Bits per Frame YUV(4:4:4) QCIF 176 ×144 608,256 176 ×144 608,256 CIF 352 ×288 2,433,024 352 ×288 2,433,024 4CIF 704 ×576 9,732,096 704 ×576 9,732,096 YUV(4:2:2) QCIF 176 ×144 405,504 88 ×144 202,752 CIF 352 ×288 1,422,016 176 ×288 811,008 4CIF 704 ×576 6,488,064 352 ×576 3,244,032 YUV(4:2:0) QCIF 176 ×144 304,128 88 ×72 76,032 CIF 352 ×288 1,216,512 176 ×144 304,128 4CIF 704 ×576 4,866,044 352 ×288 1,216,512 Video Sequence (VS) Frames Frame rate Reason for a selection AIYO 45 15 fps Low motion and dynamism FOREMAN Medium motion and dynamism MOBILE High motion and dynamism 4. Simulations Results and Analysis This section provides a detailed performance analysis of the proposed system. The JM version 15 video and HM master codec developed by the joint video team (JVT) is used as a reference H.264 and H.265 source encoder. The proposed systems is simulated using IT++ signal processing and communications library, coded in C++. To perform a fair analysis between the three schemes, several aspects of the proposed systems have been considered. Various sample of the diverse video sequences is considered and provided as an input to the considered system models. Convergence behavior in iterative source channel decoding is specifically analyzed in the proposed work with different code rates of the inner and outer channel codes. In our simulation, three different video sequences—i.e., AKIYO, FOREMAN and MOBILE as shown in Figure 6, each with three different resolution (CIF, QCIF and 4CIF) in YUV(4:2:0) format is used as testing sequences. (a) (b) (c) Figure 6. Video Sequences (a) MOBILE; (b) FOREMAN; (c) AKIYO. Brief information about the video sequences used in the simulation experiments is as follows: • Akiyo: This is a video of newscaster with extremely negligible movement and less details in the background. • Foreman: This is a video of a foreman with rapidly moving face, extensive zoom out, average details and regular structures. • Mobile: This is a video of a moving toy train with high details and dynamism. The idea of using these different diverse video sequences with different video resolutions, level of motion and dynamism is to investigate the impact of video contents on its Entropy 2021,23, 562 17 of 24 objective video quality while considering similar channel. As the extent of compression and robustness of the coded bit-stream is directly linked with its resolution, dynamism and motion content of the video stream, therefore the objective video quality performance of different video sequences is expected to be variable, while considering similar communication setup. The number of bits per frame for luminance and chrominance in different sampling format and video sequences are calculated from [ 5 ] as depicted in Table 5. The duration of each video comprising of 45 frames at the frame rate of 15 fps is considered. The macroblocks in H.264/AVC are processed in the raster scan order of a group of MBs called a slice, which represents a region of a given picture that can be processed independently of each other. In our experimental setup each slice consists of 11 Mbs while in HEVC, each frame is divided into slices which comprises CTUs. The considered slice types are intra (I), inter (P) slices. All MBs and CTUs in I slice are encoded using intra mode where as in P all MBs are coded using Intra coded mode with reference to previous frame. The encoder H.264.AVC is set at 15 fps. The resultant video frame sequence followed the pattern I1P2P3P4. . . P14 in which each 15th frame is I and the predecessor 14 are p frames. All the simulation parameters are listed in Tables 1and 3. In order to reduce the computational complexity, iterations between RSC and SBSD decoders are set to 3 and 5 for the OCM rate-1 and 3 4 respectively. The averaged results are obtained by repeating every 45 frames 160 times. The proposed system performance is exploited while considering the same overall code rate for the input H.264 encoded bit-stream. The measurement of video quality is a major task in a scenario where there is a transmission of a compressed video over noisy channels. Subjective [ 63 ] and objective [ 63 ] techniques are widely used for measuring the video quality. Human participants are required in subjective technique to assess the streaming quality and therefore it is a timeconsuming solution. On the other hand, PSNR is an objective metric for measuring the video quality when the video contents, codec and underlying communication setup remains unchanged [64]. PSNR is the ratio between the signal of the power of the original signal to that of the corrupted signal. PSNR is traditionally used as a quality metric for evaluating algorithms in multimedia streaming systems. It is generally expressed in term of Mean Square Error (MSE). The MSE of an image X having dimension of size M×N and its corrupted image Y can be expressed by the following equation. MSE =1 M∗N M ∑ i=1 N ∑ j=1 [X(i,j)−Y(i,j)] (27) PSNR(dB) = 10 ×log10 MAXX MSE (28) where MAXX is the maximum value of a pixel of image X and can be calculated as 2 B− 1 for a B bits per sample. The BER and PSNR performance of the simulation work were plotted against varying Eb/N0 values, while utilizing diverse video sequences having different resolution, motion and dynamism while employing rayleigh fading channel as a communication medium. In our simulation scenarios OCM is employed as inner code, while RSC is used as outer channel code. Furthermore, in the considered NCC scheme QPSK modulation is employed, while in NCDSTS-SP and CDSTS-SP sphere packing modulation is used in combination with DSTS. Furthermore, in order to analyze the impact of varying motion contents and dynamism of video scene on the objective video quality performance of the transmission scheme, its PSNR versus Eb/N0 dB performance trends are plotted in Figure 7a–c for NCC, NCDSTSSP and CDSTS-SP schemes, respectively, while using AKIYO, FOREMAN and MOBILE video sequences in its QCIF video resolution. In Figure 7a–c similar trend in performance variations due to employment of different video sequences, with varying motion contents Entropy 2021,23, 562 18 of 24 and dynamism is observed for all three employed schemes. Frome Figure 7a, the PSNR Vs EB/N0 [dB] is plotted for the same resolution resolution of different sequences i.e., AKIYO, FOREMAN and MOBILE considering NCC scheme. More specifically, it is observed from Figure 7a–c that the presence of high channel noise, the AKIYO video sequence with low motion contents and dynamism outperforms the FOREMAN and MOBILE video sequence with high motion contents and dynamism. This is due to the fact that in the presence of low motion contents and dynamism of video, the H.264/AVC can compress the corresponding video sequence more robustly within the allocated bit-rate budget. Furthermore, the performance of the H.264/AVC error concealment mechanism is more effective in video sequences with low motion contents and dynamism and as a result its objective quality performance will outperform. In order to analyze the impact of varying video resolution on the objective video quality performance of the transmission scheme its PSNR versus Eb/N0 dB performance trends were plotted in Figure 8a–c for NCC, NCDSTS-SP and CDSTS-SP schemes, respectively, while using AKIYO video sequence. Figure 8a is plotted for transmitting AKIYO video sequence on NCC scheme from only one Tx antenna varying the Eb/N0 values. As it is clear that the for a higher Eb/N0 , the performance of the NCC scheme in terms of PSNR is reasonable for all the three video resolutions. Moreover, due to using fast fading channel, lower the resolution of the video sequence higher its performance while NCC does not employing any differential detection at the Rx antenna. Most of the time, the SNR comes under deep fade for which the decoder cannot reliably decoder the received signal and hence the performance is degraded for the concerned scheme. In contrast, the performances of NCDSTS-SP and CDSTS-SP schemes for a very low Eb/N0 values are much better due to using differential detection and spreading codes as shown in Figure 8b,c. Moreover, CDSTS-SP scheme outperform in terms of PSNR values compare to NDSTS-SP due to the reason that CDSTS-SP achieved perfect convergence on EXIT chart using the specific inner and outer code rates specified at both the encoders. Therefore, the rule of code rate is visible on quality performance upon using the iterative decoding. The performance difference in terms of resolution used is still valid for which both the schemes outperform for lower resolution. In Figure 8a–c similar trend in performance variations due employment of different resolution videos is observed for all three employed schemes. As from Figure 8c , the PSNR reached to its peak value for a lower Eb/N0 value and remains constant for a remaining range of Eb/N0 . It is due to the fact that no matter how much the Eb/N0 is increased, the performance remains constant after the Eb/N0 range at which perfect convergence is achieved. Therefore, it is observed from Figure 8a–c that the presence of high channel noise the low resolution video outperforms its high resolution counterparts. The BER versus Eb/N0 dB performance of the NCC, NCDSTS-SP and CDSTS-SP schemes is presented in Figure 9a. BER performance is plotted against Eb/N0 values for all the three schemes. The BER values approaches to 10 −7 for a very low Eb/N0 for CDSTSSP and the same fact is visualize for PSNR from Figure 9b. As it is already discussed that CDSTS-SP is employing differential methods for recovering the SNR values at the receiver and is suitable scheme for fast fading channel while NCDST-SP is also employing differential methods but cannot obtained the perfect convergence at the considered inner and outer code rate. The worst BER performance is noticed for NCC scheme which is neither employing differential scheme nor obtained convergence on the specific inner and outer code rate. Its observed from Figure 9a that the BER performance of CDSTS-SP is best as compared to NCDSTS-SP scheme which is performing better then NCC scheme. The effects of BER on PSNR performance of the CDSTS-SP scheme can be easily observed from Figure 9b. For a very low range of Eb/N0 [dB] values, the PSNR reached to its peak values and remains constant for the rest of the Eb/N0 range which shows that the differential scheme obtained a sufficient information about a channel on iterative decoding from observing the phases of the current and the the previous symbols that can estimate the value on a lower range of Eb/N0 values. Similar performance is observed for NDSTS-SP which on slightly higher values of Eb/N0 obtained convergence and tends to be constant Entropy 2021,23, 562 19 of 24 for the rest of the Eb/N0 range while the NCC scheme is worst compared to the others and showing an increasing trend when Eb/N0 values increases. Finally, its observed from the PSNR versus Eb/N0 dB performance curves of the NCC, NCDSTS-SP and CDSTSSP schemes, presented in Figure 9b that the sophisticated system design of CDSTS-SP outperform its counterpart NCC, NCDSTS-SP schemes in terms of PSNR. More specifically, it is observed from Figure 9b that NCDSTS-SP results in PSNR gain of 6 dB and CDSTS-SP results in PSNR gain of 28 dB for Eb/N0 value of 10 dB, with reference to bench marker system design of NCC. The PSNR Vs Eb/N0 performance of the three advocated schemes—i.e., NCC, NCDSTSSP and CDSTS-SP with reference to the employed H.264 and H.265 standards is presented in Figure 9b. It can be observed from the Figure 9b, that while utilizing H.265 video coding standard, the NCDSTS-SP with enhanced transmission mechanism results in Eb/N0 gain of 20 dB with reference to the NCC coding and transmission mechanism at the objective PSNR value of 42 dB. It is important to mention that both NCC and NCDSTS-SP schemes have employed identical code rate. Furthermore, the CDSTS-SP with convergent coding mechanism achieved superior performance with reference to the competing NCDSTS-SP counterpart mechanism, with a performance gain of 16 dB at the objective PSNR grade of 42 dB. Moreover, it is observed that while employing any of the three advocated schemes— i.e., NCC, NCDSTS-SP and CDSTS-SP the maximum achievable PSNR gain through H.265 video coding standard is 45 dB, with a PSNR gain of 3 dB with reference to the identical code rate H.264 coding scheme. 5 10 15 20 25 30 35 40 PSNR 15 18 21 24 27 30 33 36 39 Eb/N0[dB] NCC(QPSK Modulation and Rayleigh Fading Channel) H.264/AVC Codec Using SBSD with OCM rate-1,RSC rate-1/3 PSNR vs Eb/N0[dB] AKIYO, Resolutoin QCIF FOREMAN, Resolutoin QCIF MOBILE, Resolutoin QCIF ( a ) PSNR-Y Vs Eb/N0 for NCC, AKIYO, FOREMAN and MOBILE Video Sequences 10 15 20 25 30 35 PSNR 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Eb/N0[dB] NCDSTS-SP(SP Modulation and Rayleigh Fading Channel) H.264/AVC Codec Using SBSD with OCM rate-1 RSC rate-1/3 PSNR vs Eb/N0[dB] AKIYO, Resolutoin QCIF FOREMAN, Resolutoin QCIF MOBILE, Resolutoin QCIF ( b ) PSNR-Y Vs Eb/N0 for NCDSTS-SP, Akiyo, Foreman and Mobile Video Sequences Figure 7. Cont. Entropy 2021,23, 562 20 of 24 5 10 15 20 25 30 35 40 PSNR -0.8 -0.4 0.0 0.4 0.8 1.2 1.6 2.0 2.4 2.8 Eb/N0[dB] CDSTS-SP(Sphere Packing Modulation and Rayleigh Fading Channel) H.264/AVC Codec Using SBSD with OCM rate-3/4 and RSC rate-4/9 PSNR vs Eb/N0[dB] AKIYO, Resolutoin QCIF FOREMAN, Resolutoin QCIF MOBILE, Resolutoin QCIF ( c ) PSNR-Y Vs Eb/N0 for CDSTS-SP, AKIYO, FOREMAN and MOBILE Video Sequences Figure 7. PSNR and BER comparison for the proposed schemes. 10 15 20 25 30 35 40 PSNR 15 18 21 24 27 30 33 36 39 Eb/N0[dB] NCC(QPSK Modulation and Rayleigh Fading Channel) H.264/AVC Codec Using SBSD with OCM rate-1, RSC rate-1/3 PSNR vs Eb/N0[dB] AKIYO, Resolutoin QCIF AKIYO, Resolutoin CIF AKIYO, Resolutoin 4CIF ( a ) PSNR-Y Vs Eb/N0 for NCC, AKIYO Video type 10 15 20 25 30 35 PSNR 3 4 5 6 7 8 9 10 11 12 13 14 15 Eb/N0[dB] NCDSTS-SP(SP Modulation and Rayleigh Fading Channel) H.264/AVC Codec Using SBSD with OCM rate-1, RSC rate-1/3 PSNR vs Eb/N0[dB] AKIYO, Resolutoin QCIF AKIYO, Resolutoin CIF AKIYO, Resolutoin 4CIF (b) PSNR-Y Vs Eb/N0for NCDSTS-SP, Akiyo Video type Figure 8. Cont. Entropy 2021,23, 562 21 of 24 5 10 15 20 25 30 35 40 45 PSNR -0.8 -0.4 0.0 0.4 0.8 1.2 1.6 2.0 2.4 2.8 Eb/N0[dB] CDSTS-SP(Sphere Packing Modulation and Rayleigh Fading Channel) H.264/AVC Codec Using SBSD with OCM rate-3/4 and RSC rate-4/9 PSNR vs Eb/N0[dB] AKIYO, Resolutoin QCIF AKIYO, Resolutoin CIF AKIYO, Resolutoin 4CIF (c) PSNR-Y Vs Eb/N0for CDSTS-SP, AKIYO Video type Figure 8. PSNR comparison for the proposed schemes. 5 10-7 2 5 10-6 2 5 10-5 2 5 10-4 2 5 10-3 2 5 10-2 2 5 10-1 2 5 BER 0 5 10 15 20 25 Eb/N0[dB] H.264/AVC and channel coded QCIF Akiyo Video Sequence Transmission BER vs Eb/N0[dB] NCC Scheme NCDSTS-SP Scheme CDSTS-SP Scheme ( a ) BER Vs Eb/N0 for NCC, NCDSTS-SP and CDSTS-SP 20 25 30 35 40 45 PSNR 0 5 10 15 20 25 30 35 40 Eb/N0[dB] H.264/AVC and channel coded QCIF Akiyo Video Sequence Transmission PSNR vs Eb/N0[dB] NCC Scheme with H.264 NCC Scheme with H.265 NCDSTS-SP Scheme NCDSTS-SP Scheme with H.265 CDSTS-SP Scheme CDSTS-SP Scheme with H.265 ( b ) PSNR Vs Eb/N0 for NCC, NCDSTS-SP and CDSTS-SP Figure 9. BER and PSNR comparison for the proposed schemes. 5. Conclusions This paper presents performance analysis of sophisticated channel coding and transmission schemes for reliable transmission of H.264/AVC and H.265/HEVC compressed video. Three different coding and transmission schemes were presented, namely NonConvergent Coding (NCC), Non-Convergent Coding assisted with Differential Space Time Spreading (DSTS) and Sphere Packing (SP) modulation (NCDSTS-SP) and Convergent Coding assisted with Differential Space Time Spreading (DSTS) and Sphere Packing (SP) modulation (CDSTS-SP). Different diverse combination of source mapping techniques and channel coding techniques were employed and their convergence behavior is analyzed with the aid of Extrinsic Information Transfer (EXIT) Charts. Furthermore, to improve the diversity gain of the transceiver advanced Sphere Packing (SP) modulation technique assisted by Differential Space Time Spreading (DSTS) is employed. The BER and PSNR performance of the proposed schemes were plotted against varying Eb/N0 Entropy 2021,23, 562 22 of 24 values, while utilizing diverse video sequences having different resolution, motion and dynamism while employing Rayleigh fading channel as a communication medium. It was observed that in the presence of high channel noise the low resolution videos outperforms its high resolution counterparts. Furthermore, it was observed that in the presence of high channel noise, the AKIYO video sequence with low motion contents and dynamism outperforms the FOREMAN and MOBILE video sequence with high motion contents and dynamism. More specifically, it is observed that while utilizing H.265 video coding standard, the NCDSTS-SP scheme with enhanced transmission mechanism results in Eb/N0 gain of 20 dB with reference to the NCC coding and transmission mechanism at the objective PSNR value of 42 dB. It is important to mention that both NCC and NCDSTS-SP schemes have employed identical code rate. Furthermore, the CDSTS-SP with convergent coding mechanism achieved superior performance with reference to the equivalent rate NCDSTS-SP counterpart mechanism, with a performance gain of 16 dB at the objective PSNR grade of 42 dB. Moreover, it is observed that the maximum achievable PSNR gain through H.265 video coding standard is 45 dB, with a PSNR gain of 3 dB with reference to the identical code rate H.264 coding scheme. Furthermore, it is observed from that the BER and PSNR versus Eb/N0 dB performance of the NCC, NCDSTS-SP and CDSTSSP schemes that the BER performance of CDSTS-SP is best as compared to NCDSTS-SP scheme, which is performing better then NCC scheme. More specifically, it is observed that the NCDSTS-SP results in PSNR gain of 20 dB with reference to the NCC coding and transmission mechanism at the objective PSNR value of 42 dB. Furthermore, the CDSTS-SP with convergent coding mechanism achieved superior performance with reference to the equivalent rate NCDSTS-SP counterpart mechanism, with a performance gain of 16 dB at the objective PSNR grade of 42 dB. Moreover, it is observed that the maximum achievable PSNR gain through H.265 video coding standard is 45 dB, with a PSNR gain of 3 dB with reference to the identical code rate H.264 coding scheme. Author Contributions: Conceptualization, N.M. and K.U.; methodology, N.M. and L.H.; software, N.M., K.U., J.N.; validation, N.M., J.F., J.N. and L.H.; formal analysis, K.U.; investigation, N.M.; resources, L.H., J.N.; data curation, J.F.; writing—original draft preparation, K.U.; writing—review and editing, N.M., J.F., J.N. and L.H.; visualization, L.H.; supervision, N.M. and L.H.; project administration, N.M. and L.H.; funding acquisition, N.M., J.N. and J.F. All authors have read and agreed to the published version of the manuscript. Funding: This research has been funded with support of the project SP2021/45, assigned to VSBTechnical University of Ostrava, the Ministry of Education, Youth and Sports in the Czech Republic. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: The financial support of the National Center of Big data and Cloud Computer (NCBC), University of Engineering and Technology, Peshawar, under the auspices of Higher Education Commission, Pakistan is gratefully acknowledged. Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References 1. Index, C. Cisco visual networking index: Global mobile data traffic forecast update, 2015–2020. In Cisco Technical Report; White Paper; Cisco System: San Jose, CA, USA, 2016. 2. Global Mobile Data Traffic Forecast. 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