A QoE Friendly Rate Adaptation Method for DASH

A QoE Friendly Rate Adaptation Method for DASH Yuming Cao 1,3, Xiaoquan You 2,3, Jia Wang 1,3, Li Song 1,3 1 Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University 2 Communication Engineering Department, Chengdu Technological University 3 Shanghai Key Laboratory of Multi Media Processing and Transmissions, Shanghai email: {caoyuming, jiawang, song li}@sjtu.edu.cn, YouXiaoquan@cdtu.edu.cn Tel: 86 18818215361, 86 21 34204468 Abstract MPEG-DASH introduced open standards into HTTP streaming, which was a huge step forward. The scope of the MPEG-DASH standard was limited to the Media Presentation Description (MPD) format of the manifest files as well as the segmentation standards for the server, but did not include the client application as part of their standards. Particularly a good representation switching algorithm at DASH client should adapt to time varying bandwidth with better QoE (Quality of Experience). In this paper, we propose a QoE friendly rate adaptation method which can achieve less switching times and more gradual quality change. To achieve these purposes, we propose a fixed-interval buffer model, which keeps the representation level unchanged whenever the media buffer size is within the preset interval. We also design a step-wise switch-up method to prevent buffer overflow and a switch-down method based on intermediate quality level to prevent buffer underflow. A quick boot algorithm is introduced to fully improve the performance of the proposed scheme. Experimental results show that the proposed algorithm can achieve more satisfactory results compared with existing ones. Index Terms Congestion control, Networking and QoS, Rate adaptation, Dynamic Adaptive Streaming over HTTP, DASH, Quality of Experience (QoE). I. INTRODUCTION Nowadays HTTP-based method has been leading the trend in the delivery of video content over the Internet [1]. Compared with traditional RTP/UDP method, the HTTP-based video streaming has several advantages, among which are that the Internet infrastructure has evolved to efficiently support HTTP and with HTTP streaming the client manages the streaming without having to maintain a session state on the server [2]. In this effort, Motion Picture Experts Group (MPEG) and 3rd Generation Partnership Project (3GPP) released their Dynamic Adaptive Streaming over HTTP (DASH) initiatives [3]. Fig. 1 shows a simple video streaming scenario between an HTTP server and a DASH client. At the server, video content is transcoded into multiple bitrates and divided into several segments. Then the client can get different segment through HTTP GET requests. The efficiency of the bitrate adaptation algorithm can considerably affect the whole system s robustness. There are four criteria to judge the effectiveness of the bitrate adaptation algorithm, which are ability to avoid playback stagnation caused by buffer underflow; Fig. 1. Dynamic HTTP Streaming Scenario high average bitrate, which is computed as follows, N i=0 µ bitrate = f(s i) t i (1) t n where t i represents the length of segment i, t n represents the length of the session and f(s i ) is the bitrate of segment i [5]; low total switching times, which is formulated as equation (2), σ 2 = N g(s i ) g(s i ) = i=0 1 if i = 0 1 if f(s i 1 ) f(s i ) 0 else (2) where σ 2 stands for the number of quality switches and s i represents the i-th segment [5]; ability to minimize the initial time of the video. So far, dynamic rate adaptation algorithms have drawn much attention, among which two main categories are formed: 1) bandwidth-based [4], and 2) buffer-based [5], [6], [7]. The bandwidth-based algorithm uses the estimated network bandwidth to make decisions to switch up or down the bitrate. There are two main drawbacks lying in this algorithm: one is that it is difficult to estimate the throughput accurately under complex network conditions, and the other is that shortterm bitrate switching would take place due to frequently varying bandwidth, thus making an adverse influence on user experience. The algorithm in [5] is a typically buffer-based rate adaptation algorithm, which is used to compare with the proposed algorithm. The buffer level based algorithm in [5],

which is referred to as the BL algorithm in the remaining part of the paper, sets a 30 second buffer (i.e., 15 segments) to compensate high bandwidth fluctuations. The maximum bitrate of the i-th segment, denoted by maxbw(s i ), is determined as follows. maxbw(s i ) = bw(s i 1 ) 0.3 if 0.00 bl i < 0.15 bw(s i 1 ) 0.5 if 0.15 bl i < 0.35 bw(s i 1 ) if 0.35 bl i < 0.50 bw(s i 1 ) (1 + 0.5 bl i ) if 0.50 bl i < 1 (3) where bl i is the buffer level at the download time of the i-th segment, i.e., the ratio of buffer size (in seconds) and the 30 seconds buffer size; and bw(s i ) is the bandwidth measured during the i-th segment of downloading. This algorithm uses a buffer of 30 seconds and different buffer levels to control the bitrate switching. In this paper, we propose an algorithm aiming at reducing the switching times and achieving a better QoE by smoothly switching from high quality to low quality. The main idea of this algorithm is to set a fixed-interval buffer in order to keep bitrate unchanged once the buffer is in this interval. The decision of switch-up and switch-down operations between different bitrates is made through preventing the buffer from either overflow or underflow. Since the initial bitrate is always set to the lowest, it takes a long time to fulfill the fixedinterval buffer to make the switch-up decision. A quick boot algorithm is designed to get the proper bitrate fitting the current bandwidth as soon as possible and a sleeping scheme is brought in to prevent the buffer overflow. From [8], we know that instead of an abrupt switching, users always prefer a gradual quality change between different quality levels. So we design an intermediate quality level in the switching-down descision Numerous experimentat results, especially the experiment compared with the algorithm in [5], demonstrate that the proposed algorithm has the following advantages: i) switching times can be dramatically reduced, especially in a frequently changing network. ii) the introduction of intermediate level representation can smoothly switching the bitrate between different quality levels, which ensures a better QoE. iii) the average bitrate is almost the same as that in [5] with much less numbers of quality switches. This paper is organized as follows. The proposed algorithm is presented in Section II in three main aspects. The fixedinterval buffer model is introduced in Section II-A. Section II-B deal with quick boot algorithm, with which we can quickly get the proper representation fitting the bandwidth without waiting for the buffer interval completely filled. The selection of proper buffer interval size is discussed in Section II-C. In Section III, the experimental results of different scenarios are given to demonstrate the advantages of the proposed algorithm over BL algorithm. Finally, the paper is concluded in Section IV., r i β i ρ i B min B low B high nextrep(r i ) getrep(ρ i ) bw(r i ) r min r max TABLE I IMPORTANT SYMBOLS USED IN THIS PAPER The representation that the i-th segment belongs to. The buffer size when the i-th segment is downloaded. The download bitrate of the i-th segment, i.e. the current bandwidth. The minimum buffer threshold denoting switching to the lowest quality once buffer is below this level. The lower bound of the fixed buffer interval. The higher bound of the fixed buffer interval. Get the next representation of the representation that the i-th segment belongs to. Get the representation fitting the predicted bandwidth. The bitrate of the representation that the i-th segment belongs to. The representation that has the lowest bitrate. The representation that has the highest bitrate. II. THE PROPOSED ALGORITHM There are two main parts of the proposed algorithm, which are the fixed-interval buffer model and quick boot algorithm. The fixed-interval buffer model is the most important part of the proposed algorithm, reflecting the main idea of the proposed algorithm. The quick boot algorithm can effectively offset the defection of the fixed-interval buffer model, making the whole scheme more robust. At last, we will discuss the proper selection of the size of the buffer interval to optimize the algorithm. We show some of the important symbols and their explanations in Table I. A. The Fixed-Interval Buffer Model We define a fixed-interval buffer B fix = [B low, B high ] and a reset threshold B min. When the buffer size, which is measured by the duration of buffered video content, is in the interval, the representation to be selected stays unchanged. When the bandwidth begins to increase, leading to the buffer size higher than B high, we should consider the following inequality: r i 1 r max && bw(nextrep(r i 1 )) < ρ i 1 (4) If the above condition is not satisfied, which means the bitrate of the next level representation is lower than the current bandwidth, it is better to stay at the current representation. But in order to prevent buffer from overflow, the system should sleep for a while, which is determined by the following function: sleep(β i 1 B high ) (5) Otherwise, we can switch to the next higher quality, i.e., r i = nextrep(r i 1 ) (6) When the bandwidth is descending, there is a factor α(α > 1) detecting the buffer level approaching B low in advance. When the current buffer size is less than α B low and the buffer size is continuing descending, i.e. bw(r i 1 ) > ρ i 1 (7)

we should switch to the intermediate representation which is between r = getrep(ρ i ) and r i 1. This mechanism can effectively enhance subjective QoE according to [8], for it can prevent an abrupt switching between the high quality and low quality and meanwhile it would not occur playback stagnation by detecting B low in advance. With this mechanism, the graduality of the quality change can be ensured, thus leading to a better experience. When the buffer size is less than B low and condition (7) is satisfied, intermediate mechanism should be discarded and the following decision should be made: r i = getrep(ρ i 1 ) (8) And if the buffer size is less than B low but the current bandwidth is higher than the bitrate of the current representation, which means the buffer size is increasing, there is no need to switch to the lower level of the representation and the next representation to be selected should be the same as the current representation. Fig. 2 shows the flowchart of the proposed algorithm, composed of the fixed-interval buffer model and quick boot algorithm, from which we can clearly see the working scheme of the algorithm. B. Quick Boot Algorithm In the proposed algorithm, we only switch to the higher quality when the buffer level is beyond B high. Since we set the initial representation as the lowest quality, it will take a long time to fulfill the buffer interval so as to switch to the proper quality fitting the bandwidth. So our quick boot algorithm is introduced to quickly switch to the representation corresponding to the current representation even if the buffer size is still lower than B low. The quick boot stage happens only if the following conditions are satisfied: 1) the current quality is not the best; 2) the buffer level is growing and lower than B low ; 3) ρ i 1 > bw(r i 1 ). The pseudo-code of the quick boot algorithm is shown in Algorithm 1, where α 1 and α 2 are factors smaller than 1 to set different conditions for different buffer level, which can set a threshold for switching to the next higher representation. From the pseudo-code, we can clearly see that quick boot algorithm can set the representation to a proper level in a short time and meanwhile the strick constraint can effectively prevent the buffer from underflow. Algorithm 1 Quick boot algorithm 1: if β i 1 < B min then 2: if bw(nextrep(r i 1 )) < α 1 ρ i 1 then 3: r i nextrep(r i 1 ) 4: end if 5: else if β i 1 < (B min + B low )/2 then 6: if bw(nextrep(r i 1 )) < α 2 ρ i 1 then 7: r i nextrep(r i 1 ) 8: end if 9: else 10: if bw(nextrep(r i 1 )) < ρ i 1 then 11: r i nextrep(r i 1 ) 12: end if 13: end if Fig. 2. The flowchart of the proposed algorithm C. The Selection Of Buffer Interval In the proposed algorithm, the length of buffer interval can affect the sensitivity of the bandwidth change. The buffer size depends on two factors, i.e. the bandwidth and the representation s bitrate. So the buffer size can be expressed as the function below: bs(t) = f(band(t), rep(t)) (9) where bs(t), band(t) and rep(t) represent buffer size, bandwidth and representation s bitrate at time t respectively. This function is a monotonically increasing function of band(t) and a monotonically decreasing function of rep(t). Thus a longer buffer interval can effectively compensate the change of buffer size caused by the fluctuation of the bandwidth. But too long buffer interval which leads to bs(t) in the buffer interval most of the time, may cause descend of average bitrate. This indicates that one should make a compromise between the switching times and average bitrate when choosing the buffer interval size.

Fig. 3. Experimental topology for dynamic HTTP streaming III. EXPERIMENT RESULTS In this paper, We implement the algorithm in a VLCbased DASH architecture [9] and all experiments have been performed using Big Buck Bunny [10]. The DASH content is encoded using X264 [11] at 13 different bitrates (200, 300, 400, 500, 600, 700, 800, 1000, 1200, 1400, 1600, 1800, 2100 kbit/s). The experimental setup for the evaluation comprises four devices: the client, the bandwidth shaping, the network emulation, and the Web server, which is shown in Fig. 3. The variables mentioned in this paper are set as follows: B min = 10s, B low = 20s, B high = 30s, α 1 = 0.6, α 2 = 0.8, α = 1.2. The buffer level based algorithm in [5] is implemented for comparison. Three scenarios have been considered in order to compare the two algorithm: 1) available bandwidth under long-term variations; 2) available bandwidth under short-term variations; 3) simulated actual network Scenario 1: Available bandwidth under long-term variations We set the initial bandwidth to 600kbit/s and keep it for 100 seconds. Then we change the bandwidth to 1600kbit/s and keeps it for 100 seconds. At last, we change the bandwidth back to 600kbit/s and keeps it for 100 seconds. The total playback time for the experiment is 300 seconds. The result of BL algorithm and the proposed algorithm in this scenario is shown in Fig. 4 and Fig. 5 respectively. The comparision showing the reaction to the available bandwidth under longterm variations of the two algorithm is shown in Table II. These results demonstrate our proposed algorithm can dramatically decrease the switching time with a slightly lower average bitrate which can be ignored. TABLE II THE REACTION TO THE AVAILABLE BANDWIDTH UNDER LONG-TERM VARIATIONS OF THE TWO ALGORITHMS Algorithm Average bitrate Switching times Stagnation time BL 827.81 kbps 58 0 s Proposed 794.70 kbps 12 0 s TABLE III THE REACTION TO THE AVAILABLE BANDWIDTH UNDER SHORT-TERM VARIATIONS OF THE TWO ALGORITHMS Algorithm Average bitrate Switching times Stagnation time BL 811.92 kbps 94 0 s Proposed 802.65 kbps 7 0 s Fig. 4. Performance of BL algorithm under long-term available bandwidth variations Fig. 5. Performance of proposed algorithm under long-term available bandwidth variations Scenario 2: Available bandwidth under short-term variations In this scenario, we also set the initial bandwidth to 600kbit/s and keep it for 100 seconds. Then we set the bandwidth alternatively between 1600kbit/s and 600kbit/s with a period of 10 seconds and keep this condition for 200 seconds. The result of BL algorithm and the proposed algorithm in this scenario is shown in Fig. 6 and Fig. 7 respectively. The comparision showing the reaction to the available bandwidth under short-term variations of the two algorithm is shown in Table III. These results demonstrate our proposed algorithm works much better in a short-term available bandwidth variation which is the case of actual wireless bandwidth environment. Scenario 3: Simulated actual network In this scenario, we use bandwidth simulator to simulate the actual bandwidth. In this experiment, we let the bandwidth randomly change around 1400kbit/s at the first 150 seconds, and then set the bandwidth down to around 500kbit/s in the following 150 seconds, which is intended to simulate the actual condition that after 150 seconds, some bandwidth-consuming application programs take over lots of bandwidth. We can use this kind of simulation to test the robustness of the proposed algorithm in the condition of network bandwidth competition.

Fig. 6. Performance of BL algorithm under short-term available bandwidth variations Fig. 8. Performance of BL algorithm under simulated actual network Fig. 7. Performance of proposed algorithm under short-term available bandwidth variations The result of BL algorithm and the proposed algorithm in this scenario is shown in Fig. 8 and Fig. 9 respectively. The comparision showing the reaction to the simulation of actual network of the two algorithm is shown in Table IV. From the results of this scenario, we can clearly see the well robustness of the proposed algorithm and its great advantages over BL algorithm in the aspect of less switching times and better QoE. TABLE IV THE REACTION TO THE SIMULATED ACTUAL NETWORK OF THE TWO ALGORITHMS Algorithm Average bitrate Switching times Stagnation time BL 862.25 kbps 90 0 s Proposed 837.09 kbps 18 0 s In these experiments, the proposed algorithm demonstrates great advantage over BL algorithm by decreasing switching times dramatically with similar average bitrate. Combined with the intermediate bitrate scheme, our proposed algorithm can provide high quality video content with better QoE. IV. CONCLUSION We have proposed a QoE friendly rate adaptation algorithm with fixed-interval buffer model for dynamic HTTP streaming. The main idea of our algorithm is keep the representation unchanged as long as the buffer is in the fixed-interval buffer and make the switching decision according to the buffer underflow Fig. 9. Performance of proposed algorithm under simulated actual network and overflow, combined with measured bandwidth condition. Meanwhile, a quick boot algorithm is adopted to select the proper representation when the buffer size has not reached the low bound of buffer. At last, numerous experiment results demonstrate the effectiveness of our proposed algorithm in the aspect of enhancing the QoE. V. ACKNOWLEDGMENTS This work was supported by National 863 project (2012AA011703), National Key Technology R&D Program of China (2013BAH53F04), NSFC (61221001, 61271221), the 111 Project (B07022) and the Shanghai Key Laboratory of Digital Media Processing and Transmissions. REFERENCES [1] A. Begen, T. Akgul, and M. Baugher, Watching Video over the Web: Part 1: Streaming Protocols, IEEE Internet Comput., vol. 15, no. 2, pp. 54C63, Mar. 2011. [2] I. Sodagar, The MPEG-DASH standard for multimedia streaming over the Internet, Transactions on MultiMedia, IEEE, vol. 18, no. 4, pp. 62C 67, 2011. [3] ISO/IEC JTC 1/SC 29/WG 11 (MPEG), Dynamic Adaptive Streaming over HTTP, w11578, CD 23001-6, Guangzhou, China, Oct. 2010. [4] C. Liu, I. Bouazizi, M. Gabbouj, Rate Adaptation for Adaptive HTTP Streaming, ACM Multimedia Systems 2011, San Jose, CA, USA, Feb. 2011. [5] C. Müller, S. Lederer, C. Timmerer, An Evaluation of Dynamic Adaptive Streaming over HTTP in Vehicular Environments, in Proceedings of the 4 th Workshop on Mobile Video (MoVid12), Feb. 2012. [6] L. De Cicco, S. Mascolo, and Palmisano V., Feedback Control for Adaptive Live Video Streaming, in Proc. ACM MMSys11, pp. 145C156, Feb. 2011.

[7] C. Zhou, X. Zhang, L. Huo, and Z. Guo, A control-theoretic approach to rate adaptation for dynamic HTTP streaming, in Proceedings of the IEEE Visual Communications and Image Processing (VCIP) Conference, 2012, pp. 1C6. [8] R. K. P. Mok, X. Luo, E. W. W. Chan, R. K. C. Chang, QDASH: A QoE-aware DASH system, ACM Multimedia Systems 2012, Chapel Hill, North Carolina, USA, Feb. 2012. [9] C. Müller, C. Timmerer, A test-bed for the dynamic adaptive streaming over HTTP featuring session mobility, in Proceedings of the ACM Mulitimedia Systems Conference(ACMMMSys2011), San Jose, California, February 23C25, 2011. [10] Big Buck Bunny Movie, http://www.bigbuckbunny.org. [11] X264, http://www.videolan.org/developers/x264.html.