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A Comparison of Quality Scheduling in Commercial Adaptive HTTP Streaming Solutions on a 3G Network. H. Riiser , H. Bergsaker , P. Vigmostad , P. Halvorsen , and C. Griwodz. Proceedings of the 4 th ACM Workshop on Mobile Video ( MoVid ) Chapel Hill, NC, USA February2012. Motivation.

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a comparison of quality scheduling in commercial adaptive http streaming solutions on a 3g network

A Comparison of Quality Scheduling in Commercial Adaptive HTTP Streaming Solutions on a 3G Network

H. Riiser, H. Bergsaker, P. Vigmostad, P. Halvorsen, and C. Griwodz

Proceedings of the 4th ACM Workshop on Mobile Video (MoVid)

Chapel Hill, NC, USA

February2012

motivation
Motivation
  • HTTP streaming increasing
  • Good performance under many situations
  • But HD mobile, where available bandwidth fluctuates a challenge
  • Overall aim is “best” viewing experience
    • Avoid buffer underruns (they cause interruptions)
    • Avoid rapid oscillations in quality (users do not like)
    • Utilize as much of the available bandwidth as possible
  • Anecdotally, noticed very different performance from different clients
fluctuating bandwidth problem2
Fluctuating Bandwidth Problem

Ferry

Metro

Question: How well do

adaptive streaming media solutions adapt?

Bus

Tram

experiment overview
Experiment Overview
  • Gather representative traces from above environment (ferry, subway, tram, bus)
    • Bitrate per second log, average delay
  • Apache module replay these exactly in closed testbed
    • That way, can change just client and get exactly the same network conditions
  • Streaming over HTTP is pull-based (rather than push-based), in that the client requests data
  • DASH – Dynamic Adaptive Streaming over HTTP
    • Not standardized at the time of this paper
  • So, choose representative players
  • Netflix and others are closed – cannot setup content to stream in testbed
    • Plus others have studied [6]
    • Plus, plus generally expected for fixed endpoints
adaptive delivery tested systems
Adaptive Delivery: Tested Systems
  • Adobe Strobe Media Playback (v1.6.328 for Flash 10.1)using HTTP Dynamic Streaming Format
  • Apple’s native iPad player (iOS v4.3.3)using native HLS format
  • Microsoft Silverlight/IIS Smooth (v4.0.60531.0 on Win7)using native Smooth format and default desktop scheduler
  • Netview Media Client (v2011-10-10)using Apple HLS format (worst case) and Netview 3G scheduler

Server

  • Apache v2.2.14
  • CodeShop Unified Streaming Platform v1.4.25, handles all encodings
  • Throttling module for trace-based bandwidth limitation, 1sec resolution
  • Dedicated Linux box, 2GB RAM, AMD 3299_ CPU

HTTP streaming

HTTP live streaming

adaptive delivery test content
Adaptive Delivery: Test Content
  • Video: Norwegian football game (i.e., soccer)
  • H.264/AVC
  • 2 second segments
  • 6 quality levels: 250, 500, 750, 1000, 1500, 3000 kbps

Question: What is “good” adaptation?

Akamai recommendations

comparison of existing quality schedulers
Comparison of Existing Quality Schedulers

Bus:

Configured for desktops

Configured for mobiles

Ferry:

conclusion
Conclusion
  • Large differences in performance, even though all have same information
  • Apple and Adobe opposites
    • Apple sacrifices high average quality for stability
    • Adobe chooses highest average quality, not stable
  • Microsoft Silverlight in between
  • Netview similar to Silverlight, but better against buffer underruns
prediction
Prediction?
  • Many people commute using the same route
  • Many mobile devices have GPS receivers
  • What about crowdsourcing the throughput on the commute paths at various times of day?

From:

“Video Streaming Using a Location-based Bandwidth-Lookup Service for Bitrate Planning”,

Haakon Riiser, Tore Endestad, Paul Vigmostad, Carsten Griwodz, PålHalvorsen, TOMCCAP

prediction1
Prediction

Metro:

Location-based bandwidth-lookup service for bitrate (video quality) planning:

Algorithm:

  • Calculate the predicted amount of data that historically can be downloaded
  • Calculate maximum steady quality without getting buffer-underruns
  • Safety: reactive algorithm fallback
future work
Future Work
  • Lots of room for adjusting, deciding “best”