Effect of Varying Segment Size on DASH Streaming Quality for Mobile User


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Effect of Varying Segment Size on DASH Streaming Quality for Mobile User

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Effect of Varying Segment Size on DASH Streaming Quality for Mobile User

  1. 1. Effect of Varying Segment Size on DASH Streaming Quality for Mobile User Yomna M. Hassan Ahmed Helmy Mohamed M. Rehan Intel Labs Egypt Cairo, Egypt Yomnax.hassan@intel.com Intel Labs Egypt Cairo, Egypt Ahmedx.helmy@intel.com Intel Labs Egypt Cairo, Egypt Mohamed.m.rehan@intel.com Abstract—In this paper we discuss the potential effects of using various segment durations of the available contents within a simulated mobile environment. OPNET modeler was used to implement a real-time test platform for mobile video streaming using OPNET system in the loop component (SITL). The realtime test platform was then used to evaluate the performance of the requests for different segment size. Dynamic Adaptive Streaming over HTTP (DASH) protocol was used to provide different test cases for video content with different segment sizes. In our experiment, we used different segments of different sizes (4, 8, and 12 sec.). Our comparison criteria include client download throughput (Bandwidth utilization), and CPU consumption. We identify the different trade-offs to be made in order to optimize the usage of available resources. Keywords—Video processing, DASH, dynamic segment size, streaming, adaptation, segment duration, user experience, OPNET. I. INTRODUCTION Media streaming is one of the most prominent applications over the internet, and is trending to become more important than social media [1]. Due to its importance, various studies have focused around how to enhance the streaming user experience, especially over mobile devices [2-4]. A main focus was the adaptation to the surrounding environmental conditions, that was later named as Dynamic Adaptive Streaming (DAS), or to be more focused on HTTP streaming, Dynamic Adaptive Streaming over HTTP (DASH) [5]. Previous research has been focused on dynamic adaptation considering changes in bandwidth, or other context related information to adhere to the environment [2], [6]. One of the points that has been discussed is how content and player (streamer) settings can be manipulated to adapt with mobile environments [7]. In [7], the authors have tested briefly the effect of video chunk on the streaming quality of experience within a pre-defined mobile environment. In this paper, we have extend this work. In this paper, we evaluate the performance of video streaming over a simulated mobile WiMAX network using different segment duration while observing the throughput (bandwidth utilization) and CPU consumption at the player side. We have implemented a HTTP adaptive player where the logic adapts to the estimated available bandwidth. We have chosen to use OPNET [8] as our simulation environment. Open source content has been used and modified as part of our experiments [9]. The paper is organized as follows. Section II highlights related work to support our choices in the field and the simulation model. This is followed by detailed explanation of the experimental setup in Section III. We detail the outcome of our experiments in the results in Section IV, followed by summary of conclusions and potential future work. II. LITERATURE REVIEW This section entails information about related work to our field of interest. We start with a background introduction about streaming over HTTP, followed by an introduction about DASH. We then give details about WiMAX structure, and why we have chosen it as the core of our simulation. We then discuss previous research that was potentially discussing changing the content structure for better adaptation. A. Media streaming over HTTP Streaming is the act of consuming (displaying) the content without entirely downloading the media [10]. HTTP became the standard protocol for media streaming on the internet as it is platform independent, and the internet became a common available resource [10]. B. Dynamic adaptive streaming over HTTP As was mentioned, the usage of media streaming applications is exceeding even social media communication [1]. Mobile users have also increased over the past decade, and are expected to grow up to 33 times over the next decade [11]. Due to the enormous change in the availability of resources, a new research trend started based on adapting the media stream according to the available resource. This new trend, called dynamic adaptive streaming over HTTP (DASH) focuses on adapting while considering trade-offs of various resources such as: bandwidth, timing, and quality [5].
  2. 2. DASH has been standardized by 3GPP, with certain specification of its structure [12]. The specification is divided as following: player over various segment durations, as well as studying the CPU consumption at the client side. III. a) Contents are stored at different bitrate levels (representations). b) Each representation includes a list of video segments that can be requested by the video client. c) It is also possible to include a list of video servers (baseURL) where the content (segments) are stored. All of this information is stored in a single Media Presentation Description (MPD) file which the client downloads and parses during streaming. The MPD file structure is shown in Figure 1. EXPERIMENTAL FRAMEWORK OPNET modeler was used to simulate the WiMAX network. Physical devices were connected to the simulated network to provide a realistic test platform using the OPNET System-in-the-loop (SITL) module. An open-sourced content “Big Buck bunny” was used for streaming. The content was split over different durations. The durations tested where 4, 8, and 12-second segments. As shown in Figure 2, the simulation platform consists of the following:  OPNET simulator (on a laptop) that simulates the wireless network.  A physical streaming server connected to OPNET using a SITL component assigned to a network interface card (NIC).  A physical video client, with a video streaming player supporting DASH, also connected to the OPNET simulated network through a dedicated SITL module assigned to a different NIC. OPNET was used to simulate one mobile video client using the trajectory shown in Figure 2. The video client receives the streamed video after it passes through the OPNET simulated network. The trajectory was chosen such that the mobile user moves gradually near to or far from the WiMAX base station in order to study the client mobility effect on the received video quality. The mobility of the client was moving at a constant speed of 40 km/hr. Figure 1. DASH MPD file structure C. Analysis of streaming over mobile enviornments Research has been focused lately on studying the effect of various adaptive streaming techniques over mobile environments. For example, there has been an empirical study comparing various streaming protocols under mobile environment [2]. Since our focus is on the effect of adapting the segment size to the mobile environment, in this section we mention the related work done in this area. In [13], segment sizing has been done based on the popularity of the segment, where larger segment size is used for more popular segments to lower the power consumed during multiple requests. In [14], another technique was done over TCP protocol that changes the segment size depending on the previous measured error rate, where they try to minimize the segment size if they get a higher frame rate error. An empirical study has been focused on viewing the different effects of mobility over media streaming regarding the quality of experience of the video. However the focus on the effect of the chunk size was limited to the effect on glitches appearance and buffering [7]. In our work, we focus more on the throughput (bandwidth) consumption of the client Video Client Video Streaming Server Figure 2. Real-time wireless video streaming platform showing the mobile client trajectory
  3. 3. IV. RESULTS After running the experiment, we analyzed the resulting statistics regarding two aspects: 1- Throughput (bits/sec), and 2- CPU consumption. A. Throughput We want to visualize the effect of changing segment size on the actual throughput received by the client. We measure throughput from the OPNET simulation side. Figure 3 represents the results we got from the simulation over segments with sizes 4s, 8s and 12s. a) The durability of the streaming (how long the stream was able to hold up with changes happening at the mobile trajectory): We noticed that the shorter the segments’ duration, the longer it was able to stream. b) Frequency of requests: although the durability of the streaming is more persistant in shorter segments, the frequency of data transfere over HTTP is increased with using shorter segment durations. This means that the amount of time spent consuming other resources such as power and CPU on the client side is greater. We will discuss that in more details in the CPU consumption analysis. B. CPU Consumption Another factor that we are considering is the percentage of CPU consumed by the streaming application through the requests made by different segment sizes. Measuring the CPU consumption has been done using the Windows Performance toolkit [15]. (a) Segment size = 4 Seconds (b) Segment size = 8 Seconds (c) Segment size = 12 Seconds Figure 3. Throughput (bits/sec) From Figure 3, 2 key elements were observed as follows: In order to unify the test, we have selected to detect the results over the first 25 seconds of streaming. This is to eliminate the consequences resulting from the video becoming incapable of streaming over larger segment durations as the video gets trapped in a loop of several attempts re-requests. Therefore, it consumes un-utilized computational power. Our goal is to focus on the power consumed during the actual streaming. We have also made sure that we unify the environment through fixing all the applications running in parallel with the player. In Figure 4, we noticed that the trend average CPU consumption overtime is lower in the 8 seconds duration content than in the 4 seconds, which is expected as we mentioned in the previous section that we expect lower frequency of requests, and therefore lower computational consumption overtime. However we notice that in the 12 seconds, the CPU consumption average gets higher. Still, the peak CPU consumption is higher at lower segment durations. We related this to the fact that although the frequency of requests have decreased, the internal processing done within the player for re-allocation of memory space and rendering increases if the segment duration exceeds the processing capabilities of the player in use. (a) Segment size = 4 Seconds
  4. 4.  Different wireless network (LTE for example) By varying such parameters, we can get more detailed description for the factors that affect mobile streaming efficiency which in turn, will allow us to provide robust conclusions. Another area of potential future work is devising an adaptation logic that better adapts to the mobility conditions discussed in the paper and enhance the overall streaming performance. (b) Segment size = 8 Seconds REFERENCES [1] [2] [3] (c) Segment size = 12 Seconds [4] Figure 4. CPU consumption percentage of overall CPU consumption V. CONCLUSIONS [5] In this paper, we presented a platform for mobile video streaming over WiMAX network using OPNET modeler. We discussed the effect of changing segment duration over DASH streaming within the simulated mobile environment by streaming contents at different segment sizes. [6] By analyzing the throughput and the CPU consumption of the client player, it was observed that smaller segment sizes have resulted in smoother streaming with limited buffering events or video cutoff during streaming. In addition, large segment sizes consume more power. We concluded that proper segment size selection during mobile video streaming can potentially improve video streaming quality. [8] As a continuation to this analysis, more factors can be discussed in the future, for example [12]  The effect of duration change over the actual content quality requested.  Other segment sizes  Different mobile speed  Different mobile trajectories  Different network conditions [9] [10] [11] [13] The effect of the changing the segment duration over the usage of the graphics device interface  [7] [14] [15] Cisco, Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update [Article], 2012–2017, 2013. Müller, C., Lederer, S., & Timmerer, C. (2012, February). An evaluation of dynamic adaptive streaming over http in vehicular environments. In Proceedings of the 4th Workshop on Mobile Video (pp. 37-42). ACM. Lewcio, B. (2014). Management of Speech and Video Telephony Quality in heterogeneous wireless networks. In Spinger. Karadimce, A., & Davcev, D. (2014). Adaptive Multimedia Delivery in M-Learning Systems Using Profiling. In ICT Innovations 2013. Springer International Publishing, 2014. 57-65. DASH Industry Forum. Overview of MPEG-DASH Standard [Article]. Available: http://dashif.org/mpeg-dash/ Georgios, G. , Pallis, E., and Grafl, E.. Media-Aware Networks in Future Internet Media.In 3D Future Internet Media. Springer New York, 2014. 105-112. Yao, J., Kanhere, S. S., Hossain, I., & Hassan, M. (2011). Empirical evaluation of HTTP adaptive streaming under vehicular mobility. In NETWORKING 2011(pp. 92-105). Springer Berlin Heidelberg. Riverbed. Network Simulation (OPNET Modular Suite) [Article]. Available: http://goo.gl/supgcM Creative Commons, Big Buck Bunny Project. Available at : http://www.bigbuckbunny.org/ C. Timmerer, C. Muller. HTTP streaming of MPEG media,Streaming day, Udine, Italy, 2010. UMTS Forum, report 44 - Mobile Traffic Forecasts 2010-2020 , January 2011. T. Stockhammer, Dynamic Adaptive Streaming over HTTP-Design Principles and Standards, World Wide Web Consortium, 2010. Yeh, Tsozen, and Zongwei Yang. "Using dynamic segmentation adjustment to improve the performance of streaming proxy servers." Broadband Multimedia Systems and Broadcasting (BMSB), 2012 IEEE International Symposium on. IEEE, 2012. Choi, Jin-Hee, Jin-Ghoo Choi, and Chuck Yoo. "Dynamic segment size adjustment for TCP performance in cellular networks." Consumer Electronics, 2005. ICCE. 2005 Digest of Technical Papers. International Conference on. IEEE, 2005. Khang Nyugen(June, 2012).Using Windows Performance toolkit in analyzing application power consumption. Available at: http://goo.gl/arVlz3