The BETSY Framework

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The BETSY Framework

• ???F? ??µ????t??, 23/5/2007
• ??µ?t???? ????at???

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Overview

• Project overview
• Summary
• Project objectives
• Methodology
• Framework
• Conclusions

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BETSY strep project  Sep 2004 Mar 2007
Participants

• NXP Netherlands
• CSEM - Switzerland
• IMEC - Belgium
• ISI - Greece
• TUK - Germany
• Siemens C-Lab - Germany
• TU/e - Netherlands
• University of Cyprus - Cyprus

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Example of home network
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Example of a hotspot
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BETSY (BEing on Time Saves energY)

• BETSY aimed to deliver the theory, models
  design methods to make
• trade-offs between
• network terminal resource consumption
• power consumption of the terminal
• timeliness of the streaming data

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Summary

• BETSY manipulates of video streams on wireless
  hand-held devices such that
• hand-held devices seamlessly adapt to fluctuating
  network conditions and available terminal
  resources
• energy consumption for processing the video is
  reduced
• True multi-media experience the device is
  required to
• handle trade-offs between the use and consumption
  of network and terminal resources such as
  bandwidth, CPU time, Buffer space, and power, and
• to guarantee to end-to-end timeliness for the
  streaming data.
• This leads to the following (next )

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Summary (cont.)

• Provide an integrated approach to real-time
  requirements for dynamic networked streaming
  systems
• Define a common resource model that can be used
  as an abstraction layer to hide lower level
  system parameters from higher level temporal
  descriptions and QoS strategies
• Understand the trade-off in energy consumption at
  the overall system level balance energy
  consumption over different sources of energy

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Trade off triangle

• Trade-offs between the use and consumption of
  network terminal resources such as
• Bandwidth
• CPU time
• Buffer space
• Power
• Guarantee end-to-end timeliness for streaming data

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Methodology

• Design reference implementation of an
  end-to-end quality-of-service framework
• Timing model for a top-down approach
• Resource model to calculate the proper
  distribution of computing resources bandwidth
  energy consumption
• Verify timing resource model ? framework
  populated by selected components or modules
• Use the frameworks mechanisms to adapt the
  processing chain to changes in the resources
• Framework its components are implemented in a
  streaming server and mobile clients
• evaluation scenarios

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BETSY functions

• Functions used by streaming applications
• Breeze is a piece of content, processed by a
  sequence of functions for processing, storing and
  communicating data items in an end-to-end
  delivery chain, on which only one entity is in
  control

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BETSY functions II

• Capturing
• Retrieving
• Recording
• Encoding
• Decoding
• Delay buffering
• Rendering
• Multiplexing
• Demultiplexing
• Transcoding
• Transporting

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Relations between BETSY functions and data types

• data types are represented as colored rectangles
• functions are represented by the rounded
  rectangles
• input and output data types with arrow from and
  to the data types

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Energy Consumption Modelling

• Desired energy models
• Desired breeze parameters?energy
• for
• Separate functional components
• Complete sub-breeze on one device
• Alternative
• Parameters f(lower level interm.)
• Intermediate params f(lower level parm.)

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Battery model
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Network Model
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MPEG-4 stream Model
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Scenario I (Evaluation)
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Composed model I
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Scenario II Evaluation
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Composed Model II
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Composition Rules

• To make trade-offs,
• latency, energy, and quality models, have to be
  combined into a single all-encompassing model
• The parameters are key to combining the models
• three independent models,
• Latency, Energy, Quality,
• each functional component, could be combined to a
  set of independent models for the entire breeze

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Compositions Rules

• Depending on the intended use of the model the
  required models of the parts can be composed into
  an end-to-end model
• Models represents a set of configurations or
  tuples of attributes
• The essential ingredients of model composition
  are
• Cartesian product of tuples when two models are
  combined freely (without any constraints).
• combinations matching on selected attributed
  (like the join of a relational database). For
  instance all components have to work with the
  same stream and hence share the same values for
  the FR, FS, IP and QP parameters.
• IA so-called producer-consumer constraint
  expressing the matching connections between
  parameters of different models such as available
  bandwidth and required bandwidth (Pareto)
  optimization after combination and abstracting
  internal parameters can be used to reduce the set
  of candidate configurations

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Composition Rules (cont.)
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Software framework
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Software frameworks common characteristics

• Recognize the benefits
• application level adaptation resource level
  management
• provide a sustained user-level quality of the
  multimedia flows
• Define a hierarchy of control elements based
• structural and temporal scope differences of the
  controlled elements
• A root element with a global knowledge of the
  system status
• Resource managers / brokers at the base, which
• receive resource allocation commands enforcing
  them
• Address QoS concepts at all domains,
• Resource-level QoS (for the network and the CPU
  resources)
• Application / video QoS (with the exception of
  PCES for an explicitly defined view of the
  latter)

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Software frameworks differences

• Design level differences at,
• structure, number, scope specific behavior of
  the intermediate control elements
• between the root system-level manager resource
  brokers at the bottom of the hierarchy
• Engineering level differences at,
• definition of the details of the interfaces of
  their software components
• realization of the inter-component communication
  mechanisms, local or distributed,

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Ozone Architecture
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Distributed ozone architecture
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Breeze Creation and Control Signaling (I)

• User instructs the system to create a new breeze
  from a source to a number of sink devices,
  streaming a selected content with a possible set
  of preferences (quality, duration).
• User interface translates the users input to a
  createBreeze() API signal, which actually
  transfers the request to a previously discovered
  BreezeManager in the distributed system.

createBreeze(src, dests, content, pref)
UI
BM
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Breeze Creation and Control Signaling (II)

• BreezeManager according to its global view of the
  system and its configuration decides on the
  acceptance of the breeze starts the creation of
  the configured elements in the appropriate
  devices (createElement() signal), connects them
  (connectElements() signal) according to the
  system configuration and returns to the user
  interface a global handle of the breeze for
  breeze handling (play(), pause(), stop() and
  destroy() signals).

BM
BM
BM
X
Create(), Connect(), Set()/Get()/Subscribe()
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Breeze Creation and Control Signaling (III)

• Each element in the control / stream chain of the
  breeze, on reception of a connect() signal from
  the BreezeManager, invokes its own startup
  signals which are mainly a number of get()/set()
  and subscribe() API calls.
• The control policy which the whole chain
  implements is then continuously running through
  the exchange of variable change events and set()
  / get() signals.

Event()
Controller
Function
Set() / Get()
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Breeze Control
Controller
Stream Data
Control Data
Function
Function
Resource mappings
RService
Resource
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Core Component Libraries

• Components built in the framework
• BreezeChain and StreamingFunction interfaces for
  
• the VLC streaming framework (www.videolan.org)
• the AXIS camera
• Resource and ResourceService interfaces for
• the CPU and the OS scheduler
• the network interface and the protocol stack
• the memory and the stream buffers
• the battery and the power consumption
• Access and protocol interfaces for
• UPnP discovery, signaling and control
• Raw TCP/UDP signaling and control

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System-level Component Libraries

• Components built with the framework for
  demonstration, validation and testing reasons
• A typical breeze manager
• A set of breeze controllers implementing various
  control policies
• A pareto modeling element
• A set of low level resource modeling elements
• A main user interface controller for device
  discovery, enumeration and breeze management
• A resource monitoring infrastructure and display
• A resource knob monitoring and control panel
• A breeze knob monitoring and control panel

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Main user interface

• Enumerate existing devices, breezes and elements
• Create and destroy breezes

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Breeze knob panel

• Monitor controller decisions and breeze
  adaptations
• Freely control any of the available knobs

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Breeze knob panel

• Monitor controller decisions and breeze
  adaptations
• Freely control any of the available knobs

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Resource knob panel

• Monitor resource status, controller decisions and
  resource adaptations
• Freely control any of the available knobs

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Resource consumption panel

• Monitor resource consumption status as a result
  of controller decisions and various knob
  adaptations

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Controller Example

• Controller (CTLS1) connected to the wireless LAN
  concrete resource interface (input), to a Pareto
  modeling element (ParetoM1) and to the encoding
  function of the source breeze chain (output)
• Based on the existence of an automatic rate
  fallback WLAN driver

Controller
ParetoM1
Encode
WLAN
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Controller Example

• Signaling of CTLS1
• Receives an event for each raw bandwidth change,
  which actually captures a very fast change in the
  link quality.
• Consults the pareto model to get the best
  configuration set for the encoder (FS, FR, IP,
  QP)
• Adapts the encoding function by applying the new
  configuration set

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Controller
ParetoM1
3
Encode
1
WLAN
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Framework overview

• software framework for QoS and resource control
  in and end-to-end stream delivery chain
• Provides the infrastructure for implementations
  of functional entities/devices with the necessary
  interfaces to control the streaming and device
  resource parameters
• Provides the means for end to end performance and
  resource consumption assessments for different
  trade-off handling and control policies
• Abstracts the main building components needed by
  the control policies making extensive reuse
  possible and guaranteeing interoperability
  between devices of different vendors
• Raises the domain of the problem hiding all
  technical details of distribution and
  inter-component communication, allowing the
  engineer to concentrate on the control policy and
  the trade-offs under study

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• Thank you

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Wireless Network Savings

• Resource consumption cost for each resource
  parameter over each resource
• Important study resource consumption behaviour
  of the transport function
• Time, Energy Primary resources
• lt abstract resources processing, storage,
  bandwidth
• We have energy costs of a stream transition
  f(costs of protocol processing, data
  packetisation, transmission)

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Configurations, ?100 kbit/s

• (IP, QP, FS, FR, PSNR, bit rate)
• (IP8, QP10, QCIF, 12.5, 26.1, 24.9),
• (IP16, QP5, QCIF, 12.5, 26.8, 47.7),
• (IP16, QP10, CIF, 25, 28.6, 95.6),
  highest PSNR
• (IP16, QP10, QCIF, 12.5, 26.1, 18.4),
• (IP16, QP15, CIF, 25, 27.8, 67.8),
• (IP16, QP15, CIF, 12.5, 26.2, 41.3),
• (IP16, QP15, QCIF, 12.5, 25.6, 12.3),
• (IP32, QP5, QCIF, 12.5, 26.8, 41.3),
• (IP32, QP10, CIF, 25, 28.5, 77.6),
• (IP32, QP10, QCIF, 12.5, 26.1, 15.2),
• (IP32, QP10, QCIF, 5, 24.3, 9.3),
• (IP32, QP15, CIF, 25, 27.8, 54.8),
• (IP32, QP15, CIF, 12.5, 26.2, 35.0),
• (IP32, QP15, QCIF, 12.5, 25.6, 10.1),
• (IP32, QP15, QCIF, 5, 24.1, 6.08)

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Overview of Video Coding
I-frames (Intra) exploit spatial correlation
within the frame P-frames (Predictive) temporal
correlation (prediction from previous
frames) Higher compression efficiency
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Impact of Intra/Predictive on Error Propagation
Concealment of lost data for example copy from
previous frame
Error propagates as later frames predict from
wrong data
Stop error propagation by inserting Intra
information (no reference to previous frames)
Lower compression efficiency of Intra -gt Bit
rate overhead
Tradeoff error robustness and coding efficiency
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Intra Frame insertion vs Gradual Intra Refreshment
Using Periodical Intra frames
Intra Period T 6
Updating in every frame an Intra portion
Intra 16
Intra information has a big impact on the
Quality-Rate modeling under network errors