Network Degradation Effects on Different Codec Types and Characteristics of Video Streaming

. Nowadays, there is a quickly growing demand for the transmission of voice, video and data over an IP based network. Multimedia, whether we are talking about broadcast, audio and video transmission and others, from a global perspective is growing exponentially with time. With incoming requests from users, new technologies for data transfer are continually developing. Data must be delivered reliably and with the fewest losses at such high speed. Video quality as part of multimedia technology has a very important role nowadays. It is inﬂuenced by several factors, where each of them can have many forms and processing. Network performance is the major degradation effect that inﬂuences the quality of resulting image. Poor network performance (lack of link capacity, high network load. . . ) causes data packet losses or diﬀerent delivery time for each packet. This work focuses exactly on these network phenomena. It examines the impact of diﬀerent delays and packet losses on the quality parameters of triple play services, to evaluate the results using objective methods. The aim of this work is to bring a detailed view on the performance of video streaming over IP-based networks.


Introduction
The growth of the Internet network is using more and more resources for performance analysis.This is simulated using different models.This work compares the performance of the network from an experimental viewpoint.The objective of this work is to analyse various impacts on transmitted video, such as packet loss, jitter, reordering.The work focuses on the presence of video and data packets in the network.It compares the impact of data loss and out of order data.The results show whether it is better to get the packets in a different order or completely lost.It compares static and dynamic video; varying quality of the transmitted video.We compare the impact of the size of the transmitted data.
We deal with objective methods for the evaluation of the quality of videos in the works.There are many attributes of the video image.These can be compared; therefore, to measure the exploits of several of the most well-known methods for the evaluation of image quality.Each method has different procedures and different metric evaluation system.We apply packet loss over predetermined steps on stream.The individual objective methods will evaluate the captured streams.The aim of the paper is to evaluate the impact of loss during transmission using different compression technologies from several different perspectives.

State of the Art
The recently growing interest in real-time service (such as audio and video) transfer through packet networks based on IP protocol has led to analyses of these services and their behavior in such networks becoming more intensive.Logically, the greatest emphasis is being put on the transfer of voice, since this service is the most sensitive to the overall network status.But on the other hand, video has become the majority part of all data traffic sent via IP networks.In general, a video service is one-way service (except e.g.video calls) so network delay is not such an important factor as in voice service.Dominant network factors that influence the final video quality are especially packet loss, delay variation and the capacity of the transmis-sion links [1].Analysis of video quality concentrates on the resistance of video codecs to packet loss in the network, which causes artefacts in the video [2], [3].On the other hand, a few factors still lack, such as a complex view of video parameters on the final video quality.In our previous works [4], [7], we focused on the quality of the triple play service prediction model implementation, where one part was dedicated to the quality of the video service.
The main motivation behind this work is to extend the mentioned computational model and bring a complex view of all video parameters like codec type, character and resolution, and their influence on negative network factors resistance.In addition to packet loss, we focused on another network disruption phenomenon called delay variation (also known as jitter).This phenomenon is very often overlooked due to de-jitter buffer implementation on the receiving side, but for better process of network situation modeling and prediction, it is good to know how it influences the final video quality.

1) Size of Digital Image Data
The volume of digital video data is usually described in the terminology of bandwidth or transfer rate.Bandwidth of a classical digital video transmission without compression is up to hundreds of Mbps.The amount of data of the picture signal is higher with an increase in resolution.The volume of data is a major problem in the transmission, processing, storage and display of video information.Digital video compared with static images is very sensitive to memory needed saving [5].
Standard television broadcasting has a frame rate of at least 25 fps (frames per second), [6].It is sufficient for the delay in perception of the human eye.Every second of the movie at resolution 1080p (Full HD) of uncompressed video can take up to tens of megabytes of memory.Video typically contains a large amount of redundant data.Those can be removed using the appropriate compression algorithms [5].

2) Video Transmission
To transfer video files, fundamentally unreliable protocols are used.The principle consists in sending and receiving data without feedback between the sender and the recipient.

Factors affecting the video transmission are:
• Latency: This is the time that elapses between sending a message from the source and adoption of the destination node.
• Packet order: Variability in the packet delivery time to the destination node causes incorrect order.
• Packet loss: This is the average number of packets that arrive at the destination node due to the state of the network.It is most often expressed as a percentage.
• Bandwidth: This expresses the capacity of the transmission channel.
• Delay: This is caused by overcrowding the packet queue on the outgoing interface [5].

3) Methods for Evaluating the Quality
In the work, we used the objective methods -PSNR and SSIM.Objective evaluation metric involves the use of the metric's computational methods, which form a "score" of the quality of the investigated video.These methods measure the physical characteristics of the video signal, such as the amplitude, timing and signalto-noise ratio.
PSNR (Peak signal-to-noise ratio) is the ratio between the maximum signal energy and noise energy.It is expressed on a logarithmic scale because different signals have different dynamic ranges.PSNR in decibels is defined by the formula [7]: where MAX is the maximum value that the pixel can take (e.g.255 for 8-bit image) and MSE is the difference between two gay-level images or video sequences.Technically, MSE reflects the diversity of the image, while PSNR expresses its identity.The strongest PSNR method is an easy and fast calculation, which is the reason why it is still used very often in scientific papers although the correlation with the human perception is worse than SSIM [6], [8].
The SSIM (Structural Similarity Index) method includes three components -the similarity of the intensity, the corresponding contrast and the corresponding structure.The combination of these three factors forms one value.This demonstrates the quality of the test video.This method differs by evaluating structural distortion and not error rate.The main reason for this difference is characteristic of the human visual system.This perceived distortion changes in the structure of the frame much better than the error rate.Since the SSIM method achieves a good correlation to the subjective impression, rating is defined in the interval [0-1], where 0 represents the worst value and 1 the best one (identity), [7].

Video Quality Evaluation
The aim of the measurement was to simulate the effect of packet loss and jitter for the video formats MPEG-2 and MPEG-4, to determine the impact on the resulting image using objective methods for measuring the quality of the video and comparing the results.We made measurements for one static and two dynamic videos of 25 seconds.All the movies were measured at a resolution of 720×576 (PAL), 1280×720 (HD) and 1920×1080 (Full HD).Static video was represented by TV news (slow motion), the first dynamic video by a space shuttle launch and the third video with the highest bitrate (60 Mbps) by an open source animated movie called Big Buck Bunny.The whole process of measuring is shown in the Fig. 1 and Fig. 2. To evaluate the quality we used the methods SSIM and PSNR.SSIM correlates better with the perception of the human eye [6].We evaluated these methods using MSU VQM Tools.As a first step, we created a stream in the VLC Player.As for the video content, streaming process RTP/UDP/IP method with MPEG2(TS) and H.264(MP4) was used.
This broadcast the video stream on the local computer interface.We captured and saved the stream to disk using another VLC Player.We saved this transfer video and tagged it as the original video.Our testing scenarios reflect the situation that can actually happen in the network.Especially the mobile networks capable of using IP architecture like UMTS and LTE reach high values of packet loss and delay variation [10].For the purpose of settings of our scenarios, we used Linux tool called Netem.Netem provides Network Emulation functionality for testing protocols by emulating the properties of wide area networks.The current version emulates variable delay, loss, duplication and re-ordering [9].

1) Packet Loss
We set the packet loss to 1 on the interface using Netem and then repeated the whole measurement.Then we repeated this step for packet loss in increments of 1 %, 2 %, ..., 10 %.

2) Jitter
We set that 25 % of packets will be delayed (results of our previous work [4] showed that approximately 25 % of all traffic had different one-way delay).We repeated the measurements for 10, 20, 30, 50, 75 and 100 ms delay variation.By streaming the videos, we set the value of the de-jitter buffer to 0 in VLC, so that the delays were real.Setting packet loss on local interface: • #tcqdisc add dev lo root netem loss 1 %.

Change packet loss on local interface:
• #tcqdisc change dev lo root netem loss 2 %.
This causes that 2 % (i.e., 2 out of 100) packets are randomly dropped.Videos for measurement were in these formats, so we did not set any additional transcoding by creating or capturing a stream.
In this example, 75 % of the packets (with a correlation of 50 %) will get sent immediately, the others will be delayed by 10 ms.In our case, correlation 50 % means that the delayed part of all data traffic is oscillated around a value of 25 %.This setting simulates the network performance behaviour more exactly.

3) Evaluation of the Results
By using the program MSU VQMT, we compared the original sample and the tested sample, which included damage caused by our settings.The program exports results into a CSV format, where we can find the value for every compared frame and the total average value for the whole video.

Results
The results of the measurements verified our prediction that not only the type of video codec has a degradation impact on video quality.On the other hand, video resolution was not proven as a significant parameter of video robustness.
The most important factor was shown to be the video code type.Video codec H.264 (MPEG-4 Part 10) is more prone to packet loss rate in the network infrastructure than the older MPEG-2.According to the results of the static video measurements, there is no big difference between the resolutions used.We detected a slight decrease in higher resolution.During the static scene, where changes were very slow, mainly the P and B frames contained approximately the same information regardless of the resolution used [8].
The first tested dynamic video achieved worse results than the static video.Again, the differences between the used resolutions were small.All the GOP frames contain more information, so packet loss significantly affects the picture distortion.That is the reason why dynamic video is generally more sensitive to data loss [5], [8].
The third video has a dynamic character, too, but a very high bitrate compared to the previous two videos.Performance of this video was very poor.High bitrate means a lot of information contained in the I, B and P frames and its loss causes significant degradation of the video quality.
For a better illustration, figures of the number 3 and 4 demonstrate the video quality results for full HD resolution.This paper follows on from our previous work [4] and extends the video prediction model that was used there.All these mentioned result were processed into the following regressive equations.

4.1.
Slow-Motion Video 2) MPEG-4 All the necessary coefficients are presented in Tab. 5.
Because measurements were performed on two dynamic videos, the following regressive equations represent a prediction model for both of them.

4.2.
Dynamic Video with Ordinary and High Bitrate Tab. 5: Coefficients for static video.

Coef
Table 6 and Tab.7 contain the coefficients for these two equations.All regressive models described here gained an R-square factor (R 2 ) higher than 90 %, which represents a high level of veracity.
The second group of measurements led to an analysis of the degradation effect of delay variation -Jitter.The results of the performed tests uncover a critical boundary of 20 ms.Above this value, a significant reduction of final video quality is observed.Due to the process of decompressing and processing the video stream on the end user side costing some time, both codecs are tolerant for small delay variation.The behaviour of dynamic videos is approximately on the same level, with bigger differences between the MPEG-2 and MPEG-4 codec when the static video was used.
Typically in the real world, de-jitter buffer is used for elimination of this phenomenon, but it is good to know how big a degradation effect on video quality has been caused particularly by the delay variation.

Conclusion
The aim of this work was to bring a detailed view of the performance of video streaming over an IP-based network.The measured results showed the relation between the video codec type and bitrate to the final video quality.These results helped us to create and extend our previous mathematical models of video streaming behaviour.The second part of the measurements was dedicated to another adverse network impact on video quality called Jitter.The results proved the importance of De-jitter buffer implementation not only for voice services but also for video streaming services.
Our future works will focus on two directions: Firstly, the new generation of video codecs such as H.265 and VP9.Due to the limitations of our evaluation MSU VQMT program, we are currently awaiting the official support for these new video codecs.The second part will be focused on analysis of the impact of security mechanisms, and on the encryption algorithm implemented to QoS parameters.Security is a highly discussed topic nowadays, and protocols such as IPsec, VPN/SSL and SRTP are becoming more and more frequently used to secure the content of voice or video, so computational mathematical models should handle this new situation.
the Operational Programme Education for Competitiveness.