A Study of Adaptive Bitrate Streaming Techniques Using Mobile Agents: Network Evaluation

Transcription

1 A Study of Adaptive Bitrate Streaming Techniques Using Mobile Agents: Network Evaluation Prof P. K. Bharti Vice Chancellor, Maharishi University of Information Technology, Lucknow, UP Abstract In this document, we provide some insight and background into the Adaptive Bit rate streaming over HTTP protocol. This document will describe how a client (Device) in the network may use the provided information to establish a streaming service for the user and works by detecting a user's bandwidth and CPU capacity in real time and adjusting the quality of a video stream accordingly. It requires the use of an encoder at Server which can encode a single source high Bit rate video at multiple bit rates. The player client switches between available multiple bit rate stored in manifest file. This document will also give some insight into the situation when multiple players compete for the available bandwidth and in spite of having constant bitrate for some situation; player oscillates between the different bitrates. We also provide some suggestions to this oscillation problem by giving a server based solution in which rate shaping at the clients takes place whenever there is oscillation in the bitrate between two players. INTRODUCTION Adaptive bitrate streaming is a technique used in streaming multimedia over computer networks. While in the past most video streaming technologies utilized streaming protocols such AS RTP with RTSP, today's adaptive streaming technologies are almost exclusively based on HTTP and designed to work efficiently over large distributed HTTP networks such as the Internet. 5 Swaranjali Publication,

2 A Study of Adaptive Bitrate Streaming Techniques Using Mobile Agents It works by detecting a user's bandwidth and CPU capacity in real time and adjusting the quality of a video stream accordingly. It requires the use of an encoder which can encode a single source video at multiple bit rates. The player client switches between streaming the different encodings depending on available resources. "The result: very little buffering, fast start time and a good experience for both high-end and low-end connections. More specifically, and as the implementations in use today are, adaptive bit rate streaming is method of video streaming over HTTP where the source content is encoded at multiple bit rates, then each of the different bit rate streams are segmented into small multi-second parts The streaming client is made aware of the available streams at differing bit rates, and segments of the streams by a manifest file. When starting, the client requests the segments from the lowest bit rate stream. If the client finds the download speed is greater than the bit rate of the segment downloaded, then it will request the next higher bit rate segments. Later, if the client finds the download speed for a segment is lower than the bit rate for the segment, and therefore the network throughput has deteriorated, then it will request a lower bit rate segment. The segment size can vary depending on the particular implementation, but they are typically between two (2) and ten (10) seconds. History Swaranjali Publication,

3 Economic Development of India Adaptive bit rate was created by the DVD Forum at the WG1 Special Streaming group in October The group was co-chaired by Toshiba and Phoenix Technologies, The expert group count with the collaboration of Microsoft, Apple Computer, DTS Inc., Warner Brothers, 20th Century Fox, Digital Deluxe, Disney, Macromedia and Akamai The technology was originally called DVDoverIP and was an integral effort of the DVD ENAV book. The concept came from storing MPEG-1 and MPEG-2 DVD TS Sectors into small 2KB files, which will be served using an HTTP server to the player. The MPEG-1 segments provided the lower bandwidth stream, while the MPEG-2 provided a higher bit rate stream. The original XML schema provided a simple playlist of bit rates, languages and url servers. The first working prototype was presented to the DVD Forum by Phoenix Technologies at the Harman Kardon Lab in Villingen Germany. The Shift to HTTP-Based Delivery One of the trends that have emerged in the streaming media industry over the past several years has been a steady shift away from classic streaming protocols (RTSP, MMS, RTMP, etc.) back to plain HTTP download. There are many examples of this trend today with community video sharing sites that employ only HTTP progressive download for media delivery. There are several strong reasons for this industry trend: Web download services have traditionally been less expensive than media streaming services offered by CDNs and hosting providers. Media protocols often have difficulty getting around firewalls and routers because they are commonly based on UDP sockets over unusual port numbers. HTTP-based media delivery has no such problems because firewalls and routers know to pass HTTP downloads through port 80. HTTP media delivery doesn't require special proxies or caches. A media file is just like any other file to a Web cache. It's much easier and cheaper to move HTTP data to the edge of the network, closer to users. Swaranjali Publication,

4 A Study of Adaptive Bitrate Streaming Techniques Using Mobile Agents Advantages Adaptive streaming, like other forms of HTTP delivery, offers the following advantages over traditional streaming to the content distributor: It's cheaper to deploy because adaptive streaming can use generic HTTP caches/proxies and doesn't require specialized servers at each node. It offers better scalability and reach, reducing "last mile" issues because it can dynamically adapt to inferior network conditions as it gets closer to the user's home. It lets the audience adapt to the content, rather than requiring content providers to guess which bit rates are most likely to be accessible to their audience. It also offers the following benefits for the user: Fast start-up and seek times because start-up/seeking can be initiated on the lowest bit rate before moving to a higher bit rate. No buffering, no disconnects, no playback stutter (as long as the user meets the minimum bit rate requirement). Seamless bit rate switching based on network conditions and CPU capabilities. A generally consistent, smooth playback experience. Four important implementations of adaptive streaming are gaining prominence Adobe HTTP Dynamic Streaming, Microsoft Smooth Streaming, Apple Live Streaming, and a newly specified industry standard called Dynamic Adaptive Streaming over HTTP (DASH), which both the Moving Picture Experts Group. We are going to explain Microsoft smooth streaming as follows. Microsoft Smooth Streaming Smooth Streaming, an IIS Media Services extension, enables adaptive streaming of media to Silverlight and other clients over HTTP. Smooth Streaming provides a high-quality viewing experience that scales Swaranjali Publication,

5 Economic Development of India massively on content distribution networks, making true HD 1080p media experiences a reality. Smooth Streaming is the productized version of technology first used by Microsoft to deliver on-demand video of the 2008 Summer Olympics for NBCOlympics.com. By dynamically monitoring local bandwidth and video rendering performance, Smooth Streaming optimizes content playback by switching video quality in real-time. Viewers with high bandwidth connections and modern computers can experience full HD 1080p quality streaming, while others with lower bandwidth or older computers receive an appropriate stream for their capabilities. The end result is simple: viewers across the board enjoy a compelling, uninterrupted streaming experience that leads to extended audience engagement and increased revenue opportunities. Smooth Streaming uses the simple but powerful concept of delivering small content fragments (typically 2 seconds worth of video) and verifying that each has arrived within the appropriate time and played back at the expected quality level. If one fragment doesn't meet these requirements, the next fragment delivered will be at a somewhat lower quality level. Conversely, when conditions allow it, the quality of subsequent fragments will be at a higher level. To create Smooth Streaming presentations, the same source content is encoded at several quality levels, typically with each level in its own complete file, using a compression tool such as Expression Encoder or a product from one of our many partners. Content is delivered using a Smooth Streaming-enabled IIS origin server. After the IIS origin server receives a request for media, it will dynamically create cacheable virtual fragments from the video files and deliver the best content possible to each client. The benefit of this virtual fragment approach is that the content owner can manage complete files rather than thousands of presegmented content files. Smooth Streaming provides media companies with a better way to make full HD on the web a reality. It offers code-free deployment and Swaranjali Publication,

6 A Study of Adaptive Bitrate Streaming Techniques Using Mobile Agents simplified content management for content creators and content delivery networks. For content viewers, the improved video viewing experience brings the reliability and quality of HD television to their favorite video websites. Client server interaction using HTTP protocol Smooth Streaming Player Behavior under Unrestricted available Bandwidth Two successive, say video, requests sent at times t1 and t2 (t1<t2) with timestamps t 1 and t 2 (t 1 < t 2) respectively Swaranjali Publication,

7 Economic Development of India The playback buffer duration (in seconds) for video at time t2 is estimated as: B(t2) = B(t1) - (t2-t1) + (t 2 - t 1) Smooth Streaming Player Behavior under persistent change in available Bandwidth Rate adaptation occurs after long delays The player estimates available bandwidth using a running average of the per-fragment TCP throughput measurements Smooth stream Buffer Fill Level Playback delay increases over time whenever playback buffer gets empty Player does not skip fragments Swaranjali Publication,

8 A Study of Adaptive Bitrate Streaming Techniques Using Mobile Agents Two Smooth Streaming Player Compete Players keep oscillating between rates even when available bandwidth is constant Synchronization can cause simultaneous bitrate drops Fairness issue: one stream may get much lower bitrate than the other. Weaknesses in adaptation logic Large delay in responding to persistent available bandwidth variations Erratic rate adaptations under short-term variations Oscillations and unfairness when multiple players compete Smooth streaming problem of Oscillation when two player competes and proposed solution Smooth streaming problem when two players are running on client, two or more adaptive streaming players can be unstable when they compete for bandwidth. The root cause of the instability problem is that, in Steady-State, a player goes through an ON-OFF activity pattern in which it overestimates the available bandwidth. We propose a server-based traffic shaping method that can significantly reduce such oscillations without significant (or any) loss in bandwidth utilization. The shaper is only activated when oscillations are detected, and it dynamically adjusts the shaping rate so that the player should ideally receive the highest available video profile while being stable. We evaluate the proposed Swaranjali Publication,

9 Economic Development of India method experimentally in terms of instability and the utilization comparing with the unshaped case. An adaptive streaming player typically operates in one of two states: The Buffering-State, during which the player requests a new video chunk when the previous chunk has been downloaded, and the Steady-State, during which the player requests a new video chunk periodically, once per chunk duration (T seconds). During Steady-State, the player attempts to maintain a constant playback buffer size. Such periodic requests lead to an ON-OFF activity pattern in which the player is either ON, downloading a chunk, or OFF, staying idle. In parallel, the player estimates the network available bandwidth based on the per-chunk TCP download throughput, and it relies on that estimate to select the bitrate (video profile) for subsequent chunks. If two or more video players share the same bottleneck link, and depending on the temporal overlap of their ON-OFF periods, they may both overestimate the available bandwidth. Consider, for instance, the case that the ON period of player X overlaps with the OFF periods of other players; player X will then overestimate the available bandwidth because its downloads do not share the bottleneck with other players. Player X would then decide to switch to a higher video profile, even though that bitrate may not be sustainable, resulting in congestion. In that case player X would switch back to a lower profile, causing oscillations. Fundamentally, the problem is that player X cannot correctly observe the available bandwidth when it is in the OFF state. We propose a server-based traffic shaping method that aims to eliminate the root cause of the instability problem, i.e., to avoid the OFF periods during Steady-State. Specifically, when the server detects that a player oscillates between different profiles, it activates a shaping module (or shaper ) for that player. The shaper limits the throughput for each chunk to the encoding rate of that chunk; so, as long as the available bandwidth is higher than the shaping rate, the download duration will be roughly equal to the chunk duration T. Consequently, the player will remain ON even when it operates in Steady-State. We do not make any assumptions about the location and capacity of the shared bottleneck or about the number of competing players. On the other hand, Swaranjali Publication,

10 A Study of Adaptive Bitrate Streaming Techniques Using Mobile Agents we assume that a player receives most successive video chunks from the same video server (so that the shaping module can maintain the required state for each player); this may not be true in the presence of caches or when the player requests different chunks from different CDNs/servers. Finally, our traffic shaping method is server-based, not requiring any cooperation from the client, and so it is entirely different than clientbased adaptation methods. Conclusion We proposed a shaping mechanism that can be used to mitigate the instability problem of adaptive streaming players when they share a network bottleneck and compete for bandwidth. The stabilization mechanism aims to eliminate the root cause of the problem, i.e., the ON- OFF download pattern of adaptive streaming players in Steady-State. The proposed mechanism does not require the players cooperation and it is entirely implemented at the server. The proposed shaping mechanism results in a major reduction in the instability metric, practically stabilizing the competing players, without a significant utilization loss. Future Work Continue the analysis of commercial players to understand working And identify weaknesses Expand study of multi-player competition Design and implement an adaptive steaming adaptation logic that can address all previous issues References en.wikipedia.org/wiki/adaptive_bitrate_streaming 3. Video compression for flash and apple devices. 4. Alex Zambelli, IIS Smooth Streaming Technical Overview, bit.ly/smoothstreamzambelli 5. C. Siva Ram Murthy, B.S. Manoj, Ad Hoc Wireless Networks : Architectures and Protocols, Prentice Hall Publishers, May 2004, ISBN X Swaranjali Publication,

11 Economic Development of India 6. C.-K. Toh, Ad Hoc Mobile Wireless Networks: Protocols and Systems, Prentice Hall publishers, December 2001, ISBN C. Perkins and P. Bhagwat, Highly Dynamic Destination- Sequenced Distance-Vector Routing (DSDV) for Mobile Computers. In Proc. of the ACM SIGCOMM, October pdf 8. Shree Murthy, J.J. Garcia-Luna-Aveces, "A Routing Protocol for Packet Radio Networks," Proc. ACM International Conference on Mobile Computing and Networking, pp , November, C.-C. Chiang, Routing in Clustered Multihop, Mobile Wireless Networks with Fading Channel, Proc. IEEE SICON 97, Apr. 1997, pp [online] The Secan Lab, University of Luxembourg, Luxembourg [online] The Secan Lab, University of Luxembourg, Luxembourg D B. Johnson, D A. Maltz, and Y. Hu, "The dynamic source routing protocol for mobile ad hoc network," Internet-Draft, April C.E. Perkins, E. Royer, and S.R. Das, "Ad hoc on demand distance vector (AODV) routing," Internet Draft, March /draft-ietf-manet-aodv-05.txt 16. Samir R. Das, Charles E. Perkins, Elizabeth M. Royer and Mahesh K. Marina. "Performance Comparison of Two On-demand Routing Protocols for Ad hoc Networks." IEEE Personal Communications Magazine special issue on Ad hoc Networking, February 2001, p V_and_DSR-Perkins.pdf Swaranjali Publication,

12 A Study of Adaptive Bitrate Streaming Techniques Using Mobile Agents 17. David B Johnson and David A Maltz. Dynamic source routing in ad hoc wireless networks. In Imielinski and Korth, editors, Mobile Computing, volume 353. Kluwer Academic Publishers, Haas Z.J, A new routing protocol for the reconfigurable wireless network. In Proceedings of the 1997 IEEE 6th International Conference on Universal Personal Communications, ICUPC '97, San Diego, CA, October 1997; pp Kimaya Sanzgiri, Bridget Dahill, Brian Neil Levine, Clay Shields and Elizabeth M. Belding royer. "A Secure Routing Protocol for Ad Hoc Networks" (ARAN) In International Conference on Network Protocols (ICNP), Paris, France, November Swaranjali Publication,