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Bandwidth-aware Prefetching for Proactive Multi-video Preloading and - - PowerPoint PPT Presentation

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Bandwidth-aware Prefetching for Proactive Multi-video Preloading and Improved HAS Performance Vengatanathan Krishnamoorthi 1 , Niklas Carlsson 1 , Derek Eager 2 , Anirban Mahanti 3 , Nahid Shahmehri 1 1 Linkping university, Sweden 2 University

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Bandwidth-aware Prefetching for Proactive Multi-video Preloading and Improved HAS Performance

Vengatanathan Krishnamoorthi1, Niklas Carlsson1, Derek Eager2, Anirban Mahanti3, Nahid Shahmehri1

1 Linköping university, Sweden 2 University of Saskatchewan, Canada 3 NICTA, Australia

  • Proc. ACM Multimedia, Brisbane, Australia, Oct. 2015
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Users of the Web are very impatient and want instantaneous response for every action ...

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Users of the Web are very impatient and want instantaneous response for every action …

– Loading a web page

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Users of the Web are very impatient and want instantaneous response for every action ...

– Loading a web page – Response to search query

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Users of the Web are very impatient and want instantaneous response for every action ...

– Loading a web page – Start playing a video – Response to search query

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Users of the Web are very impatient and want instantaneous response for every action ... Delays in executing these actions leads to ...

– Annoyed users – Loading a web page – Start playing a video – Response to search query

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Users of the Web are very impatient and want instantaneous response for every action ...

– Dissatisfaction with the service and service providers

Delays in executing these actions leads to ...

– Annoyed users – Loading a web page – Start playing a video – Response to search query

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Users of the Web are very impatient and want instantaneous response for every action ...

– Terminated sessions Lost revenue!!

Delays in executing these actions leads to ...

– Dissatisfaction with the service and service providers – Annoyed users – Loading a web page – Start playing a video – Response to search query

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Users of the on-demand video streaming services ...

watch the beginning of several videos (~5 seconds) before actually watching a video until the end1.

1- L. Chen, Y. Zhou and D. Chiu. A study of user behavior in online vod services. Computer Communications, 2014.

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Users of the on-demand video streaming services ...

watch the beginning of several videos (~5 seconds) before actually watching a video until the end1.

1- L. Chen, Y. Zhou and D. Chiu. A study of user behavior in online vod services. Computer Communications, 2014.

  • Knowing these patterns, popular streaming services
  • ffer several related videos to chose from, based on

– current video choice – user viewing history – popular videos in the geographical area – many other information sources ...

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However, there is a startup time associated with every new video ...

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However, there is a startup time associated with every new video ... and we all know that it is annoying to wait

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However, there is a startup time associated with every new video ... and we all know that it is annoying to wait

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In order to reduce startup times and improve user retention

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In order to reduce startup times and improve user retention

  • Effective prefetching strategies are required
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In order to reduce startup times and improve user retention

  • Effective prefetching strategies are required
  • Alternate videos must be readily available for

playback and played instantaneously

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  • Effective prefetching strategies are required
  • Alternate videos must be readily available for

playback and played instantaneously

  • Prefetching must be quality-adaptive and

have no negative effects on the current video’s playback

In order to reduce startup times and improve user retention

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  • Effective prefetching strategies are required
  • Alternate videos must be readily available for

playback and played instantaneously

  • Prefetching must be quality-adaptive and

have no negative effects on the current video’s playback

  • These goals need to be achieved with the

current state-of-the-art

In order to reduce startup times and improve user retention

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Contributions

  • We present a HAS-based solution that:
  • enables quality adaptive prefetching and instantaneous

playback of alternative videos

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Contributions

  • We present a HAS-based solution that:
  • enables quality adaptive prefetching and instantaneous

playback of alternative videos

  • improves the playback quality of the current video, by

addressing the well known on-off problem in HAS

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Contributions

  • We present a HAS-based solution that:
  • enables quality adaptive prefetching and instantaneous

playback of alternative videos

  • improves the playback quality of the current video, by

addressing the well known on-off problem in HAS

  • ensures stall free playback of the current video with

improved playback experience

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Contributions

  • We present a HAS-based solution that:
  • enables quality adaptive prefetching and instantaneous

playback of alternative videos

  • improves the playback quality of the current video, by

addressing the well known on-off problem in HAS

  • ensures stall free playback of the current video with

improved playback experience

  • Our policy classes captures a diverse set of use cases
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Contributions

  • We present a HAS-based solution that:
  • enables quality adaptive prefetching and instantaneous

playback of alternative videos

  • improves the playback quality of the current video, by

addressing the well known on-off problem in HAS

  • ensures stall free playback of the current video with

improved playback experience

  • Our policy classes captures a diverse set of use cases
  • We characterize and show the benefits of our prefetching policies

through our proof-of-concept implementation

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HTTP-based Adaptive Streaming (HAS)

  • HTTP-based streaming
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HTTP-based Adaptive Streaming (HAS)

  • HTTP-based streaming

– Video is split into chunks

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HTTP-based Adaptive Streaming (HAS)

  • HTTP-based streaming

– Video is split into chunks – Easy firewall traversal and caching

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HTTP-based Adaptive Streaming (HAS)

  • HTTP-based streaming

– Video is split into chunks – Easy firewall traversal and caching

  • HTTP-based adaptive streaming

– Clients adapt quality encoding based on buffer/network conditions

Chunk1 Chunk2 Chunk4 Chunk3 Chunk5

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HTTP-based Adaptive Streaming (HAS)

  • HTTP-based streaming

– Video is split into chunks – Easy firewall traversal and caching

  • HTTP-based adaptive streaming

– Clients adapt quality encoding based on buffer/network conditions – Support for interactive VoD

Chunk1 Chunk2 Chunk4 Chunk3 Chunk5

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On-off switching in HAS

  • Most HAS players perform ON-OFF switching

based on two buffer thresholds: Tmin and Tmax

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On-off switching in HAS

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  • Most HAS players perform ON-OFF switching

based on two buffer thresholds: Tmin and Tmax

  • If buffer > Tmax

– Suspend download

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On-off switching in HAS

buffer > Tmax

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  • Most HAS players perform ON-OFF switching

based on two buffer thresholds: Tmin and Tmax

  • If buffer > Tmax

– Suspend download

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On-off switching in HAS

buffer > Tmax

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  • Most HAS players perform ON-OFF switching

based on two buffer thresholds: Tmin and Tmax

  • If buffer > Tmax

– Suspend download

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On-off switching in HAS

buffer < Tmin

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  • Most HAS players perform ON-OFF switching

based on two buffer thresholds: Tmin and Tmax

  • If buffer > Tmax

– Suspend download

  • If buffer < Tmin

– Resume download

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On-off switching in HAS

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buffer < Tmin

  • Most HAS players perform ON-OFF switching

based on two buffer thresholds: Tmin and Tmax

  • If buffer > Tmax

– Suspend download

  • If buffer < Tmin

– Resume download

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Issues with on-off switching in HAS

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  • Although thresholds on the buffer is beneficial, on-off

switching has been shown to lead to:

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Issues with on-off switching in HAS

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  • Although thresholds on the buffer is beneficial, on-off

switching has been shown to lead to: – Unfair bandwidth allocation – Under utilization of bandwidth – Unnecessary fluctuations in quality adaptation

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Prefetch alternative videos during off periods

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  • Allow instantaneous playback of alternative videos
  • In addition, prefetching during off periods:
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Off period

Slow-start and ramp up

Prefetch alternative videos during off periods

  • Allow instantaneous playback of alternative videos
  • In addition, prefetching during off periods:

– Avoids the need to ramp-up from slow-start

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With prefetching, data is downloaded faster and the next off period is reached sooner

Prefetch alternative videos during off periods

  • Allow instantaneous playback of alternative videos
  • In addition, prefetching during off periods:

– Avoids the need to ramp-up from slow-start – Client remains active throughout the duration

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  • Allow instantaneous playback of alternative videos
  • In addition, prefetching during off periods:

– Avoids the need to ramp-up from slow-start is – Client remains active throughout the duration

Prefetch alternative videos during off periods

Greater slope  faster download

With prefetching, data is downloaded faster and the next off period is reached sooner

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Prefetching policies

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  • In order to control the number of prefetched chunks and the

time at which alternate videos will be available for playback, we consider three broad classes of prefetching policies: – Best-effort – Token-based – Deadline-based

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Prefetching policy: Best-effort

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  • Prefetching rules:

– Prefetch alternative chunks when T ≥ Tmax and r(T – Tmin) > prefetchedchunk size

1 2 4 5 6 7 8

Best effort

1 2 3 1 2 1 2 Tmin T0 Tmax 9 10 1 2 1 Alt.1 1 Alt.2 1 Alt.3 1 Alt.4

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Prefetching policy: Best-effort

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  • Prefetching rules:

– Prefetch alternative chunks when T ≥ Tmax and r(T – Tmin) > prefetchedchunk size – Number of chunks per alternate video is controlled by parameter ‘n’

1 2 4 5 6 7 8

Best effort

1 2 3 1 2 1 2 Tmin T0 Tmax 9 10 1 2 1 Alt.1 1 Alt.2 1 Alt.3 1 Alt.4

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Prefetching policy: Token-based

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  • Prefetching rules:

– Prefetch alternative chunks when T ≥ Tmax and r(T – Tmin) > prefetchedchunk size

1 Alt.1 Tmin T0 Tmax 1 Alt.2 1 Alt.3 1 Alt.4 1 2 4 5 6 7 8

Token- based

1 2 3 3 4 1 2

Token alt:1 Token alt:2

9 10 3 4

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Prefetching policy: Token-based

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  • Prefetching rules:

– Prefetch alternative chunks when T ≥ Tmax and r(T – Tmin) > prefetchedchunk size – Token determines which alternative video to prefetch

1 Alt.1 Tmin T0 Tmax 1 Alt.2 1 Alt.3 1 Alt.4 1 2 4 5 6 7 8

Token- based

1 2 3 3 4 1 2

Token alt:1 Token alt:2

9 10 3 4

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Prefetching policy: Token-based

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  • Prefetching rules:

– Prefetch alternative chunks when T ≥ Tmax and r(T – Tmin) > prefetchedchunk size – Token determines which alternative video to prefetch – Time Δ determines time between prefetching of alternative videos

1 Alt.1 Tmin T0 Tmax 1 Alt.2 1 Alt.3 1 Alt.4 1 2 4 5 6 7 8

Token- based

1 2 3 3 4 1 2

Token alt:1 Token alt:2

9 10 3 4

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Prefetching policy: Deadline-based

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  • Prefetching rules:

– Strict deadlines by which ‘n’ chunks of alternative videos (and ‘m’ chunks of current video) must be downloaded

Time 1 Alt.1 Tmin T0 Tmax 1 2 4 5 6 7

Deadline- based

1 2 3 1 2 3

Deadline alt:1

4 8 9 1 2

Deadline alt:2

1 Alt.2 1 Alt.3 1 Alt.4

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Prefetching policy: Deadline-based

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  • Prefetching rules:

– Strict deadlines by which ‘n’ chunks of alternative videos (and ‘m’ chunks of current video) must be downloaded – Quality of the streaming video is adapted to satisfy the deadlines based on an optimization framework

Time 1 Alt.1 Tmin T0 Tmax 1 2 4 5 6 7

Deadline- based

1 2 3 1 2 3

Deadline alt:1

4 8 9 1 2

Deadline alt:2

1 Alt.2 1 Alt.3 1 Alt.4

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Prefetching policy: Deadline-based

  • Prefetching rules:

– Strict deadlines by which ‘n’ chunks of alternative videos (and ‘m’ chunks of current video) must be downloaded – Quality of the streaming video is adapted to satisfy the deadlines based on an optimization framework

Playback quality of streamed video Playback quality alternative video chunks

The optimization formulation above originally appeared in our ACM MM 2014 paper . Quality-adaptive Prefetching for Interactive Branched Video using HTTP-based Adaptive Streaming In Proceedings of the ACM International Conference on Multimedia (ACM Multimedia),Orlando, FL, Nov. 2014.

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Policy characterization

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  • All experiments performed with at least one competing flow
  • Generated from a large file download from a second server
  • Results are averages over 20 experiments
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Policy characterization

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  • All experiments performed with at least one competing flow
  • Generated from a large file download from a second server
  • Results are averages over 20 experiments
  • Example results
  • 4 Mbps (shared) link
  • 1 competing flow
  • 150ms RTT
  • Tmin/ Tmax = 8/12
  • n (chunks to prefetchper alternative video) = 2
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Policy characterization: Number of alternative videos

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  • Best-effort policy

– Moves to the next alternative video after 2 chunks of the previous alternative video is completed

Figures: Download completion time of the n=2 chunks of each alternative video

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Policy characterization: Number of alternative videos

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Figures: Download completion time of the n=2 chunks of each alternative video

  • Token-based policy

– Moves to the next alternative video only when the next token is released

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Policy characterization: Number of alternative videos

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  • Token-based policy

– Moves to the next alternative video only when the next token is released – Prefetches more chunks of alternative videos in the absence of new tokens

Figures: Download completion time of the n=2 chunks of each alternative video

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Policy characterization: Number of alternative videos

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  • Deadline-based policy

– Deadlines every 20s

Figures: Download completion time of the n=2 chunks of each alternative video

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Policy characterization: Number of alternative videos

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  • Deadline-based policy

– Deadlines every 20s – Evenly spaced download completions, respecting their download deadlines

Figures: Download completion time of the n=2 chunks of each alternative video

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Policy characterization: Number of alternative videos

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Figures: Download completion time of the n=2 chunks of each alternative video

  • Deadline-based policy

– Deadlines every 20s – Evenly spaced download completions, respecting their download deadlines – When the deadline is satisfied, the player moves ahead with the next deadline

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Policy characterization: Playback quality of streamed video chunks

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Policy characterization: Playback quality of streamed video chunks

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Medium quality

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Policy characterization: Playback quality of streamed video chunks

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  • Best-effort policy achieves better playback rates than the

naïve player

High quality

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Policy characterization: Playback quality of streamed video chunks

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  • Best-effort policy achieves better playback rates than the

naïve player

  • Token-based policy also achieves better playback rates

than the naïve player

High quality

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Policy characterization: Playback quality of streamed video chunks

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  • Best-effort policy achieves better playback rates than the

naïve player

  • Token-based policy also achieves better playback rates

than the naïve player

  • Comparatively, deadline-based policies achieve lower

playback qualities due to deadline constraints

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  • Lowest-quality prefetching always chooses the lowest

encoding available

Policy characterization: Playback quality of alternate video chunks

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  • Lowest-quality prefetching always chooses the lowest

encoding available

  • Deadline-based policy trades-off quality of both streamed

and alternate videos to achieve the deadlines

Policy characterization: Playback quality of alternate video chunks

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Impact of network conditions: Bandwidth

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  • All policies adapt playback quality based on bandwidth

Quality of streamed video Quality of alternate video Stall probabilities

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Impact of network conditions: Bandwidth

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  • All policies adapt playback quality based on bandwidth
  • Deadline-based policy consistently trades-off playback

quality in order to meet deadlines

Quality of streamed video Quality of alternate video Stall probabilities

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Impact of network conditions: Bandwidth

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  • All policies adapt playback quality based on bandwidth
  • Deadline-based policy consistently trades-off playback

quality in order to meet deadlines

  • Best-effort and token-based policies perform slightly better

than the naïve player at low bandwidths

Quality of streamed video Quality of alternate video Stall probabilities

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Impact of network conditions: RTT

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  • The video flow experiences increasing RTTs while the

competing flows RTT remains constant at 50ms

Quality of streamed video Quality of alternate video Stall probabilities

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Impact of network conditions: RTT

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  • In general, TCP throughput decreases with increasing RTTs,

as shown by playback qualities

Quality of streamed video Quality of alternate video Stall probabilities

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Impact of network conditions: RTT

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  • In general, TCP throughput decreases with increasing RTTs,

as shown by playback qualities

  • Playback stalls experienced at high RTTs, although all three

policies out perform the naïve player

Quality of streamed video Quality of alternate video Stall probabilities

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Startup times

  • With prefetching, startup times are low and independent of

throughput or RTT

  • Fetch time of the chunk from cache ~0.1 second, the

additional time is required to change player states

Startup time/SD (seconds) Prefetched chunk 0.6/0.15 No prefetching, 2000 Kb/s, 150ms RTT 10/4.1 No prefetching, 4000 Kb/s, 150ms RTT 5.8/2.3 No prefetching, 6000 Kb/s, 150ms RTT 4.0/1.2 No prefetching, 8000 Kb/s, 150ms RTT 3.6/1.4

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Startup times

  • With prefetching, startup times are low and independent of

throughput or RTT

  • Fetch time of the chunk from cache ~0.1 second, the

additional time is required to change player states

  • Startup times decrease with increasing bandwidth, but are

always constrained by the larger RTT and network conditions to reach the server

Startup time/SD (seconds) Prefetched chunk 0.6/0.15 No prefetching, 2000 Kb/s, 150ms RTT 10/4.1 No prefetching, 4000 Kb/s, 150ms RTT 5.8/2.3 No prefetching, 6000 Kb/s, 150ms RTT 4.0/1.2 No prefetching, 8000 Kb/s, 150ms RTT 3.6/1.4

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Other experimental results

  • Also performed experiments under wide range of other

scenarios, including – different buffer sizes (4/6, 8/16, 12/24, 12/30 seconds) – different real-world bandwidth traces – different number of competing flows – different OSes running different TCP versions

  • Our conclusions and relative performance across policies

remain consistent in all scenarios

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Conclusions

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  • We have designed, implemented and evaluated a HAS-based

solution, which: – enables quality-adaptive prefetching and instantaneous playback of alternative videos – leads to perceptible gains for the streamed video in terms of stall-free playback and better playback quality

  • Considered three different policy classes and two quality

adaptation methods to cater for different real-world use cases

  • Overall, our solutions improve the bandwidth utilization and

playback experience by leveraging off periods to download alternative videos that are most likely to be watched

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Say

Bandwidth-aware Prefetching for Proactive Multi-video Preloading and Improved HAS Performance

Our source codes are available for download here: http://www.ida.liu.se/~nikca/papers/mm15.html